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‘Outrageous agers’ by Rosy Martin and Kay Goodridge

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we hope they can try something in other directions to explore more possibilities.

The team's questionnaire is a bit too broad and should be set in a context, or we can suggest that the team could use experiential interviews, such as going through their day with some elderly people and using observation to find out what difficulties the ageing population is experiencing in their lives. This is something that is difficult to observe through questionnaire research. Some elderly people are not very good
at expressing themselves or are too shy to do so, and a direct questionnaire survey may cause secondary psychological damage to the elderly group.

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1.可能会对“变老”做出笼统的概括。如果

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1.可能会对“变老”做出笼统的概括。如果这是你项目的原因，你需要更清楚地表达出来。考虑项目开发中的利害关系。“不可控制的衰老”这个标题也有负面含义:这是故意的吗?老一代的活力、欲望和能动性呢?该项目还表达了对同理心的强烈渴望。认识到这是一种不稳定的立场/情绪——衰老不是一个固定的类别——似乎很重要。

1-变老的动态（积极）
站在更人文的情怀面对衰老
衰老只是一种成长的过程 是更丰富的阅历

2.推测性的表演
3.查找research

3.

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探索协作思维、研究和创意过程如何应对复杂

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探索协作思维、研究和创意过程如何应对复杂和具有挑战性的问题:作为一个跨学科团队，你们聚集在一起，从年轻一代的角度探索全球老龄化社会的社会和文化背景。你们清楚地表达了弥合代沟的目标，提高对变老意味着什么的认识和理解，使年轻人和老年人的关系更密切(更关心)。这种动机来自于良好的背景研究。然而，该项目可能会对“变老”做出笼统的概括。如果这是你项目的原因，你需要更清楚地表达出来。考虑项目开发中的利害关系。“不可控制的衰老”这个标题也有负面含义:这是故意的吗?老一代的活力、欲望和能动性呢?该项目还表达了对同理心的强烈渴望。认识到这是一种不稳定的立场/情绪——衰老不是一个固定的类别——似乎很重要。确保项目真正具有凝聚力和合作性需要进一步关注。目前，每个团队成员都在为沉浸式体验贡献一个元素。虽然每个成员的实践和兴趣都能得到体现是件好事，但我担心你们会以一种原子化的方式工作。你将如何确保一个集体的反应，使各部分能够配合在一起和连接?不同的部分将如何在空间中移动和激活?最重要的是，我担心完全实现这种沉浸式空间的目标超出了项目的范围。时间表看起来很紧。你能考虑把项目的结果变成一种推测性的表演吗?例如，想象新的世界和空间?

批判性地与同行、学术和行业网络接触，以研究当代全球和本地问题:你们以批判性、谨慎和反思的方式聚集在一起，与同行网络接触。您的面板链接展示了与学术和行业研究的良好合作。但你的研究的理论方面可以进一步帮助加深你的项目的严谨性和细微差别。例如，看看简·Gallop关于衰老和性的作品(性、残疾和衰老，杜克大学，2019年)，还有艾达·阿普尔布鲁克、路易斯·布尔乔亚或埃莉诺·安坦等老艺术家的作品?他们的工作可能会很有趣。考陶尔德艺术历史学家乔·阿普林(Jo Applin)目前正在研究衰老、抽象和女权主义实践。在生成经过深思熟虑的、建设性的和慷慨的同行反馈方面做得很好——有帮助地构建到具体的发展领域。

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Explore how collaborative thinking, research and creative processes can respond to complex and challenging issues:  as an interdisciplinary team you have come together to explore the social and cultural contexts of a global ageing society from the perspective of a younger generation. You clearly express the aim of bridging the generational gap, of enhancing awareness and understanding of what it means to grow old, of bringing young and old into closer (more caring) relation. This motivation has emerged from good contextual research. Yet the project risks making sweeping generalizations about ‘growing old’. If this is the reason for your project this needs to be more clearly articulated. Consider what is at stake in the development of your project. The title ‘uncontrollable ageing’ also has negative connotations: is this intentional? What of the vitalities, desires, and agencies of the older generation? The project also articulates a strong desire for empathy. Recognizing this as a slippery position/emotion — and ageing as not a fixed category — seems important.  Ensuring that the project feels genuinely cohesive and collaborative requires further attention. Currently each team member is contributing an element to the immersive experience. While it is good that each member’s practices and interests are represented, I am worried you will be working in an atomized way. How will you ensure for a collective response that enables for the parts to fit together and connect? How will the different parts be moved through and activated in the space? Most significantly, I worry that the aim to fully realize this immersive space is beyond the scope of the project. The timeline looks tight. Could you think about changing the outcome of the project into a speculative performance, for example, that imagines new worlds and spaces?

Critically engage with peer, academic and industry networks to examine contemporary global and local concerns: you have come together critically, carefully, and reflectively to engage with your peer networks. Your Padlet links demonstrate fine engagement with academic and industry research. But the theoretical aspect of your research could go further to help deepen the rigour and nuance of your project. For example, see the work of Jane Gallop on ageing and sexuality (Sexuality, Disability and Aging, Duke UP, 2019), and what of older artists such as Ida Applebroog, Louise Bourgeois, or Eleanor Antin? Their work could be interesting to engage with. Jo Applin, art historian at The Courtauld, is currently researching ageing and abstraction and feminist practices. Well done on generating considered, constructive, and generous peer feedback — helpfully structured into specific areas for development.

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How frailty leads to loneliness and isolation | Age UK

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What does it feel like to be old and alone?

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【淘宝】https://m.tb.cn/h.U7DaAiu?tk=wogjd202tSu CZ0001 「北京同仁堂官方旗舰店官网正品品牌一洗黑染发剂膏自己在家染头发」点击链接直接打开 或者 淘宝搜索直接打开

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【淘宝】https://m.tb.cn/h.U7DaAiu?tk=wogjd202tSu CZ0001 「北京同仁堂官方旗舰店官网正品品牌一洗黑染发剂膏自己在家染头发」点击链接直接打开 或者 淘宝搜索直接打开

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how to advertise elderly?Keep it simple. Visuals and short phrases are best for most older people so don't keep them bewildered with too much information at once. ...Make sure your website is easy to navigate. ...Make your site easy to see.

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how to advertise elderly?Keep it simple. Visuals and short phrases are best for most older people so don't keep them bewildered with too much information at once. ...Make sure your website is easy to navigate. ...Make your site easy to see.

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Calcium Supplements & Tablets | Vitabiotics®

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Vitabiotics have a wide range of Calcium supplements for all ages & requirements, including for bones and menopause. 3 for 2 on all orders.

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广告诱导成老年人最大的隐患

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广告诱导成老年人最大的隐患

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3月15日晚，2021央视"3·15"晚会曝光"老人手机里的安全陷阱"，内存优化大师等手机app被点名。据央视报道，步入数字经济时代，老年人手机中存在诸多"安全陷阱"，在老年人使用手机过程中，经常有app提示手机经常弹出"安全提示"，又或者是"诱人广告"--如红包抢钱等。

据央视3·15报道，据悉现在不少老年人在使用手机看新闻，看小说时，手机里总会自动蹦出一些"安全提示"、"病毒""垃圾""内存严重不足"等信息，于是有些老人会按照提示去下载一些"手机管家软件"。但是在下载后，这些"手机管家软件"会频繁推送一些"诱人"的广告--红包领钱、金币赚钱、走路挣钱等，而且手机也开始经常卡顿，电池也不耐用了。

对此中国电子技术标准化研究院网安中心测评实验室的副主任何延哲介绍，这类APP表面上看起来是在清理手机垃圾，背地里实则在不断大量获取手机里的信息。何延哲表示：它会收集很多的个人信息，甚至会频繁高频率地上传，手机会变得更卡，老人用户就会再中招，再去下载这种清理类App，形成了恶性循环。号称是手机管家，却短时间内高频读取手机信息，故意让手机卡顿，从而达到让用户不断点击下载App的目的。不明就里的老人更容易上当，不断点击下载。

何延哲介绍："在应用内这种广告来诱导你下载，对于年轻人来讲，这种诱导性的分发，还有一定的识别能力。但对老年人，从生理角度来讲，可能眼神不太好，关闭不掉这些广告，就触碰、下载了。"而且更大的危害在于，这类App驻扎在老人的手机里，不断在后台发送用户数据信息，对老人们进行用户画像，打上"容易被误导和诱导"的群体标签。于是，各种低俗、劣质，甚至带有欺骗套路的广告和内容就会源源不断地推送到老人的手机上。

在推进智能化服务适应老年人需求的过程中，手机软件的适老化是关键环节之一。南都商业数据新闻部对"中老年人手机软件使用现状"展开问卷和走访调研。调研数据显示，在中老年手机软件使用障碍TOP10中，排名前三的问题集中在广告纷扰方面，依次为诱导下载、广告太多影响使用，以及广告没有关闭键，容易误触。

自去年底以来，关于"数字鸿沟"及互联网应用的适老化改造已成为热点话题。刚刚结束的全国两会上，不少委员、代表关注适老化改造，南都记者梳理发现，全国两会中关于"适老化"的提案/建议目前已经覆盖智能应用、智能终端、公共服务、社区、家庭等产品和场景，如扩大适老化智能设备供给、为老人提供智能化产品培训和辅导、公共服务中提供线上线下多样选择、坚决打击针对老年人的网络诈骗、引导技术适老与智慧社区智慧家居结合等，同时有委员、代表指出，帮助老人克服"数字鸿沟"应动员社会各方力量共同推进。

"互联网应用的适老化和无障碍改造，不仅造福数亿老年互联网用户，是企业面向特定用户群体的服务能力和使用体验的再升级，更是帮助行业和企业，推动互联网产业立足国家快步进入老龄化社会的大环境，完成后人口红利发展期的转型。"近日，全国政协委员、搜狗CEO王小川回复南都记者时表示。

对此，王小川建议，"一方面，政府通过监管底线问题保安全。对于针对老年人的电信诈骗和侵犯个人信息等网络犯罪，坚决打击，重拳治理。另一方面，坚定不移地扶持新技术在社会治理中的有益应用。同时，用鼓励性产业政策调动企业积极性。对于普遍适用的用户需求，形成推荐性行业标准。对于积极投身适老化改造的产品及服务，纳入示范应用目录，给予推广渠道、优惠财税等激励政策，使适老化改造迸发出市场的活力。"

出品：南都商业数据新闻部

责任编辑：

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央视315曝光手机软件坑老，广告诱导已成老人最大困扰之一_何延哲

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3月15日晚，2021央视"3·15"晚会曝光"老人手机里的安全陷阱"，内存优化大师等手机app被点名。据央视报道，步入数字经济时代，老年人手机中存在诸多"安全陷阱"，在老年人使用手机过程中，经常有app提示手机经常弹出"安全提示"，又或者是"诱人广告"--如红包抢钱等。 ...

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袁隆平摔了一跤引发病重过世，老年人如何预防骨质疏松？_腾讯新闻

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袁隆平摔了一跤引发病重过世，老年人如何预防骨质疏松？

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精神慰藉方面：在老年人烦心事的倾诉对象这一块,总体而言,老年人的自我精神支持也已经超过了家庭的精神支持,居于第一位。自我养老与家庭养老在生活照料、精神慰藉方而此消彼长的态势,表明自我养老是弱化掉的家生活照料功能和精神慰藉功能的主要替代者。

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精神慰藉方面：在老年人烦心事的倾诉对象这一块,总体而言,老年人的自我精神支持也已经超过了家庭的精神支持,居于第一位。自我养老与家庭养老在生活照料、精神慰藉方而此消彼长的态势,表明自我养老是弱化掉的家生活照料功能和精神慰藉功能的主要替代者。

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Spiritual comfort: In terms of who the elderly confide in for their worries, overall, the self-support of the elderly has overtaken the spiritual support of the family and is in the first place. The trend of self-support and family support in life care and spiritual comfort shows that self-support is the main substitute for the weakened home care and spiritual comfort function.

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老年人精神慰藉服务严重不足，农村老年人精神孤独问题尤为突出，空巢老年人（老年夫妇户、独居老人）占老年人口的比例为51.3%，其中农村为51.7%

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老年人精神慰藉服务严重不足，农村老年人精神孤独问题尤为突出，空巢老年人（老年夫妇户、独居老人）占老年人口的比例为51.3%，其中农村为51.7%

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Spiritual comfort services for the elderly are seriously inadequate, and the problem of mental loneliness among the elderly in rural areas is particularly prominent, with 51.3% of the elderly population being empty nesters (elderly couple households and elderly living alone), including 51.7% in rural areas

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在中国，老年人面临的问题有哪些？_百度知道

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End of life care is support for people who are in the last months or years of their life.End of life care should help you to live as well as possible until you die and to die with dignity. The people providing your care should ask you about your wishes and preferences and take these into account as they work with you to plan your care.They should also support your family, carers or other people who are important to you.

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End of life care is support for people who are in the last months or years of their life.End of life care should help you to live as well as possible until you die and to die with dignity. The people providing your care should ask you about your wishes and preferences and take these into account as they work with you to plan your care.They should also support your family, carers or other people who are important to you.

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Body experience
Improving empathy
Mental health
Ageing

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中国人口老龄化趋势/Trends in China's ageing population

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中国人口老龄化趋势/Trends in China's ageing population

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国际上通常的看法是，当一个国家或地区60岁及以上老年人口占人口总数的10%，或65岁及以上老年人口占人口总数的7%，就意味着这个国家或地区进入老龄化社会。按照上述标准，我国已于1999年进入老龄社会，是较早进入老龄社会的发展中国家之一。目前，我国65岁及以上老人所占比重已经接近10%，而根据2013年中国人类发展报告的预测，到2030年，我国65岁以上的人口占全国总人口的比重将提高到18.2%。
The common international view is that a country or region enters an ageing society when 10% of the total population is aged 60 and above, or 7% of the total population is aged 65 and above. According to the above criteria, China has entered an ageing society in 1999, and is one of the developing countries that entered an ageing society earlier. At present, the proportion of people aged 65 and above in China is already close to 10%, and according to the forecast of the 2013 China Human Development Report, the proportion of people aged 65 and above in the country's total population will increase to 18.2% by 2030.

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21 世纪是人口老龄化的时代。国际上通常的看法是，当一个国家或地区60岁及以上老年人口占的10%，或65岁及以上老年人口占人口总数的7%，就意味着这个国家或地区进入老龄化。按照上述标准，我国已于1999年进入老龄社会，是较早进入老龄社会的发展中国家之一。目前，我国65岁及以上老人所占比重已经接近10%，而根据2013年中国人类发展报告的预测，到2030年，我国65岁以上的人口占全国总人口的比重将提高到18.2%。人口老龄化问题关乎我国未来经济社会的长远发展，必须对当前我国人口老龄化的趋势及可能带来的影响进行深入分析，以便及时调整相应的政策和措施加以应对。
一、我国人口老龄化的成因
表面上看，人口老龄化产生的原因比较简单，即出生率下降、人口寿命提高。但是，受社会发展的客观因素影响，不同国家或地区人口老龄化的形成背景有一定区别。我国人口老龄化的进程既受到人口转变的一般因素影响，也受到计划生育的特殊因素影响。
（一）我国人口老龄化的一般因素：人口转变规律
从理论上讲，人口转变是指在现代化进程中，人口的再生产类型从高出生率和高死亡率逐渐向低出生率和低死亡率转变的过程。一般来说，按照出生率和死亡率的高低不同，可以把人口转变划分为三个不同阶段：第一阶段的主要特征是高出生率、高死亡率和低人口增长率；第二阶段是高出生率、低死亡率和高人口增长率；第三阶段是低出生率、低死亡率和低人口增长率。从国内外的大部分研究成果来看，人口转变的起点一般是高位均衡阶段，而终点则是低位均衡阶段。可以简单理解为，人口转变进入低位均衡阶段时就表明人口转变基本完成，同时人口的年龄结构也开始逐渐进入老龄化阶段。根据国际惯例，可以认为2003年是我国人口死亡率变化的拐点。考虑"自然增长率标准"和"死亡率拐点标准"这两大标准，在2006年我国就已经完成了人口转变。
（二）我国人口老龄化的特殊因素：计划生育政策
如果没有计划生育政策，那么我国人口转变过程持续的时间可能会更加漫长，由此导致人口老龄化的发展进程也会更加缓慢。纵观我国经济社会发展态势，我国人口老龄化进程加快的原因在于两个方面的"夹击效应"，一方面是政府实施的计划生育政策加速了生育率的降低，另一方面是经济社会发展带来的家庭生育决策的改变。从70年代末期我国实施计划生育政策开始，通过三十多年不断的宣传和教育，并辅之以相应的经济手段和一定的行政手段，计划生育政策取得了良好的效果，初步估计受该政策影响我国少生了4亿人左右。国际社会普遍认为，我国是世界上生育率下降非常显著的国家之一，并完成了从典型的高出生率国家到低出生率国家的转变，这充分说明了计划生育政策在我国人口结构变化过程中起到了非常重要的作用。
二、我国人口老龄化的现状
我国是较早进入老龄化社会的发展中国家，与其他西方发达国家相比，我国人口老龄化具有不同的特点。
（一）老年人口的基数大
2014 年，我国60岁及以上老年人口达到2.12亿，占总人口的15.5%，这个规模跟现在的欧洲三大国（德国、法国、英国）的人口总量是相当的。按照目前的趋势发展，可以预计，到本世纪中叶的时候，我们国家60岁及以上老年人口可能达到4.5-4.7亿，这取决于今后的出生率包括生育政策的调整。当然，我们的高龄老人还有失能、半失能老人也多，也是总量大的一个特点。
（二）人口老龄化速度快
这主要表现在两个方面，一方面是"未富先老"，另一方面是老龄化的增速快。发达国家进入人口老龄化时经济发展水平已经比较高，具有较强的经济实力，各方面的社会保障制度比较完善，人口老龄化的速度也比较缓慢，所经历的时间长达几十年甚至上百年，从而为人口老龄化做好了准备，能够很好的应对人口老龄化。我国是在经济发展水平不高的情况下进入了人口老龄化，可以说是"未富先老"。1999年我国进入老龄化社会时的人均GDP只有1000美元左右，而发达国家是在人均GDP达到5000-10000美元水平时进入老龄化的。并且，今后我国将经历一个老龄化率急剧拉升的阶段。从2000年到2010年，我国老龄人口比重提高了3个百分点。而2010年之后的40年，每十年老年人口比重将分别提高4.7、8.0、5.2、5.3个百分点。到2030年时，我国的老龄化水平将和2010年的德国水平相当，到2040年时，我国的老龄化水平将和2010年的日本相当。
（三）老龄化进程不平衡
不平衡包括三个方面，一是城乡的不平衡，农村老年人口比例高于城镇，农村老年人口比重15.6%，比城镇高4.7个百分点。二是区域不平衡，东部快于西部，上海是我国最早进入老龄化的地区，而整个西部地区直到2012年才进入老龄化社会。如果按照户籍口径，现在上海老龄化水平已经达到了27%，而西部地区如西藏和新疆还没有进入老龄化社会。三是结构不平衡，我国出生人口存在大起大落的现象，人口年龄结构失衡现象比较突出，老龄化进程也有明显阶段性的不均衡。新中国成立以来有三次生育高峰，第一次是1950-1957年，第二次是1962-1972年，第三次是1981-1990年，在第一次出生高峰和第二次出生高峰叠加的时候，老年人口比例将一下子急剧拉升提高8个百分点。
（四）老龄化承载基础弱
首先，制度准备不足。我国的养老政策体系处在初创和完善阶段，近几年，我国修订老年权益保障法，部门和各地出的法规规章以上的规范性文件有二百多项，然而实际上在一些领域还是有空白。其次，资金准备不足。社会养老保险作为一项兜底的保障措施，按照当前的征缴和支出水平，以及考虑到欠费、统筹、管理等方面的问题，局部地区存在收不抵支的风险。再次，人才准备不足。我国的养老和护理员工作收入比较低，职业认同感较差，而且由于规范化培训尚未广泛推开，人才供需缺口比较大。同时，养老机构的管理人才也较为匮乏，经验不足。最后，设施准备不足。2014年我国每千名老人拥有的养老床位数只有26张，而发达国家是50-70张，其中还有一部分是社区照料中心的一些非护理床位。
三、我国人口老龄化的趋势
根据我国人口年龄结构变化以及考虑到可能的生育政策调整，预测到21世纪末，我国人口老龄化的发展将大致经历四个阶段：快速发展阶段、急速发展阶段、缓速发展阶段及高位稳定阶段。
（一）快速发展阶段（2015-2021年）
从2015年到2021年为快速发展阶段，同时我国人口处于轻度老龄化阶段。在此阶段，我国老年人口迎来第一个增长高峰，将增长到2.58亿人。这一阶段增加的老年人口属于"50后"，他们的思想观念、收入水平、生活方式不同于"30后"、"40后"，不仅消费能力强，而且只有少部分人赶上计划生育，大多数有3个及以上子女。这些子女是"50后"老年人经济来源的主要补充，但这些子女目前是社会的中坚力量，不太可能为其父母提供家庭养老服务。不过，他们是发展老龄金融的重要客户群体，这一阶段是我国老龄产业发展的黄金战略准备期。
（二）急速发展阶段（2022-2030年）
从2022年开始到2030年我国老年人口将迎来第二个增长高峰，也是21世纪老年人口增长规模最大的一次，由2.58亿增长到3.71亿人。老龄化水平在2024年达到20.3%，开始过渡到中度老龄化阶段。到2023年前后，老龄人口将超过少儿人口，标志着我国从主要抚养儿童的时代迈入主要抚养老人的时代。这一阶段的老年人口主要是"60后"。这批人经历了严格的计划生育，子女数量锐减，城市老年夫妇平均不到1个子女，农村老年夫妇平均也只有2个子女左右。这批人思想观念开放、生活方式现代化、经济实力也比较雄厚。
（三）缓速发展阶段（2031-2053年）
从2031年到2053年为缓速发展阶段，我国人口迈入中度老龄化阶段。在此阶段，我国总人口进入负增长阶段，人口总量开始减少，老年人口增长态势放缓，由3.71亿人增长到4.87亿人。这一阶段增加的老年人口大多是"70后"，他们中很多人拥有巨大的老龄金融资产，将是老龄产业的直接消费者和间接消费者。在此阶段，我国老龄产业发展进入成熟期。
（四）高位稳定阶段（2053-2100年）
从2053年到2100年为高位稳定发展阶段。在这一时期，我国人口持续处于重度老龄化阶段。老年人口增长期结束，由4.87亿人减少到3.83亿人，人口老龄化水平始终稳定在1/3上下，高位运行，形成一个稳态的重度老龄化平台期。
四、我国人口老龄化的影响
联合国有关报告指出，人口老龄化是人类历史上前所未有的一场无声的革命，足以影响或改变未来。毫无疑问，人口老龄化将成为我国基本国情的组成部分，成为我国实现两个一百年奋斗目标进程中必须考虑的一个长期的变量。
（一）人口老龄化对经济增长的影响
首先，老龄化直接导致劳动力供给的减少，潜在经济增长率面临下行的压力。特别是人口红利形成的劳动力成本低这一比较优势下降乃至消失，将明显影响制造业出口利润。2012年是我国劳动力供给的一个拐点，15-59岁人口第一次绝对减少了345万。按照目前的趋势预测，2010年到2050年，我国劳动年龄人口将由9.4亿缩减到7.1亿。其次，老龄化使人口结构老化，居民储蓄率的变化呈现出先增后降的倒U型，由于退休金的替代率下降速度更快，最终导致全社会储蓄率下降，影响社会资本的形成，也会使消费能力下降，导致需求不足。在此情况下，政府财政面临收入减缓和养老、医疗等社会保障支出增加的双重压力，将严重制约政府直接投资和间接引导投资的能力。
（二）人口老龄化对结构调整的影响
完成压缩过剩产能，淘汰落后产能，发展现代服务业等促进产业转型升级的任务都需要调整劳动力的结构，提高劳动力的素质。从常理上讲，企业更倾向于向年轻的员工投资、对年轻人进行培训，而老龄化将使劳动力结构老化，企业对人力资本的提升意愿降低，从而影响产业转型升级。研究表明，随着年龄的增长，劳动力个体的生产率呈现先增后降的变化态势，一般是在40岁达到顶峰，此后明显下降。今后一个时期，我国劳动年龄人口中位数将快速上升，到2030年45岁以下的劳动年龄人口将减少四分之一，劳动力将整体老化，将对结构调整产生不利影响。
（三）人口老龄化对创新驱动的影响
新常态下，由要素驱动、投资驱动转向创新驱动，是必由之路。从目前的情况看，我国劳动力的绝对数是在下降的，全社会固定资产投资总额增速从前些年的25%下降到近两年的20%，去年则进一步降至15.1%。因此，只能从全要素生产率上做文章，特别是在创新上做文章。而人口劳动力的老化将影响社会活力，明显弱化全社会创新创造能力。从需求方面看，消费对经济增长的重要性会越来越大，而人口老龄化可能不利于消费领域新产品、新技术的应用和推广，年龄越大，对新技术的接受能力就差一些，不愿意接受新生事物。
（四）人口老龄化对社会保障的影响
首先，老年人口增加将使政府公共财政在基本养老金、退休金、保险费补贴和医疗方面的支出增加。据测算，2015年到2050年期间，全社会用于养老、医疗、照料、福利与设施方面的费用占GDP的比例将来可能会接近欧洲国家的平均水平。其次，人口老龄化改变家庭结构和规模，抚养比上升，削弱家庭的养老功能，导致家庭养老的风险增加，家庭的代际矛盾显性化。在这种情况下，2013年我国修订了《老年人权益保障法》，规定年轻人要常回家看看。从全社会的角度分析，如果生育政策不变，未来我国劳动人口和老年人口之比将从上世纪90年代的7:1发展到本世纪中叶的不足1.5:1。社会抚养结构的变化将深刻改变社会公共资源的分配格局，容易诱发代际之间利益分配的矛盾。
五、我国人口老龄化的应对
首先，要把老龄社会作为21世纪中国的一个重要国情认真对待。政府有关部门及全社会必须充分认识人口老龄化挑战的严峻性，树立老龄意识，增强应对人口老龄化和老龄社会挑战的紧迫性和自觉性。要切实从老龄社会这一基本国情出发，把应对老龄社会的挑战列入未来中国的发展战略之一。

第二，要充分做好应对老龄社会的各项准备。必须把解决老龄社会的各种矛盾和问题纳入全面建设小康社会和社会主义现代化建设的总体发展战略。要立足当前，在完善政策、加大投入、加快发展老龄事业的同时，健全和完善老龄工作体制，切实解决制约老龄事业发展的体制性问题。

第三，要加快老年社会保障体系建设。在2030年人口老龄化最严峻时期到来之前，要在全国城乡基本建立起符合我国国情、适应社会主义市场经济体制要求的老年社会保障体系，确保城乡老年人养老、医疗问题的妥善解决。

第四，要大力发展老龄产业。制定老龄产业行业发展规划，颁布实施国家对老龄产业的扶持保护政策，建立老龄产业发展管理体制。立足城乡社区发展养老服务业，培育老年服务中介组织，培养专业化的为老社会服务队伍。同时，大力研究开发老年消费品，培育老年用品市场。

第五，要加强对老龄社会的前瞻性和战略性研究。建立综合性国家级研究机构，组织相关学科研究人员，把人口老龄化和老龄社会作为国家的重大宏观战略课题，立项进行攻关研究。

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我国人口老龄化趋势及其影响

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Focus on Identity and Try to be more humanized

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Focus on Identity and Try to be more humanized

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Based on social reality and the real experience of members in this group- the stories of our elders, we decide to focus on the topic "ageing".

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老年犯罪的性别比例/Gender Ratio of Crime in Older Adults

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老年犯罪的性别比例/Gender Ratio of Crime in Older Adults

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Ms. O, 78 years old

"Prison is my oasis, a place where I can completely relax. Although I have no freedom here, I have nothing else to worry about. I have a lot of people here to talk to, and I'm provided with three nutritious meals a day.

According to a survey conducted by the Japanese government, more than half of all elderly shoplifters live alone.

They may have a house, they may have a family. But that doesn't mean there's a place where they can feel at home. Not being understood is the biggest common denominator among the elderly criminals.

A significant number of people commit crimes with the goal of "going back to prison. It is more difficult for them to survive when they are released from prison, but prison life is more secure.

The cost of housing older people in prison is rising compared to the steady stream of older people who deliberately commit crimes and enter prison.

During the day, prisons often hire staff to take care of the elderly and wash them, but at night the guards are left to do the work. In some places, prison correctional officers are no different than full-time caregivers in nursing homes.

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World Health Organization - The economic impact of population ageing in different regions

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World Health Organization - The economic impact of population ageing in different regions

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https://apps.who.int/iris/bitstream/handle/10665/186463/9789245565048_chi.pdf

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In 1950 there were 2.5 billion people on the planet. Now in 2022, there are around 8 billion. By the end of the century, the UN expects a global population of around 10.4 billion. This visualization of the population pyramid makes it possible to understand this enormous global transformation.

Population pyramids visualize the demographic structure of a population. The width represents the size of the population of a given age; women on the right and men on the left. The bottom layer represents the number of newborns and above it, you find the numbers of older cohorts. Represented in this way the population structure of societies with high mortality rates resembled a pyramid – this is how this famous type of visualization got its name.

In the darkest blue, you see the pyramid that represents the structure of the world population in 1950. Two factors are responsible for the pyramid shape in 1950: An increasing number of births broadened the base layer of the population pyramid and a continuously-high risk of death throughout life is evident by the pyramid narrowing towards the top. There were many newborns relative to the number of people at older ages.

The narrowing of the pyramid just above the base is testimony to the fact that more than 1 in 5 children born in 1950 died before they reached the age of five.¹

Through shades of blue and green the same visualization shows the population structure over the last decades up to 2018. You see that in each subsequent decade the population pyramid was larger than before – in each decade more people of all ages were added to the world population.

If you look at the green pyramid for 2018 you see that the narrowing above the base is much less strong than back in 1950; the child mortality rate fell from 1-in-5 in 1950 to fewer than 1-in-20 today.

In comparing 1950 and 2018 we see that the number of children born has increased – 97 million in 1950 to 143 million today – and that the mortality of children decreased at the same time. If you now compare the base of the pyramid in 2018 with the projection for 2100 you see that the coming decades will not resemble the past: According to the projections there will be fewer children born at the end of this century than today. The base of the future population structure is narrower.

We are at a turning point in global population history. Between 1950 and today, it was a widening of the entire pyramid – an increase in the number of children – that was responsible for the increase of the world population. From now on is not a widening of the base, but a ‘fill up’ of the population above the base: the number of children will barely increase and then start to decline, but the number of people of working age and old age will increase very substantially. As global health is improving and mortality is falling, the people alive today are expected to live longer than any generation before us.

At a country level “peak child” is often followed by a time in which the country benefits from a “demographic dividend” when the proportion of the dependent young generation falls and the share of the population of working age increases.³

This is now happening on a global scale. For every child younger than 15 there were 1.7 people of working age (15 to 64) in 1950; today there are 2.6; and by the end of the century, there will be 3.6.⁴

Richer countries have benefited from this transition in the last decades and are now facing the demographic problem of an increasingly larger share of retired people who are not part of the labor market. In the coming decades, it will be the poorer countries that can benefit from this demographic dividend.

The change from 1950 to today and the projections to 2100 show a world population that is becoming healthier. When the top of the pyramid becomes wider and looks less like a pyramid and instead becomes more box-shaped, the population lives through younger ages with a very low risk of death and dies at an old age. The demographic structure of a healthy population at the final stage of the demographic transition is the box shape that we see for the entire world in 2100.

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在生命的最后阶段，

甚至在死前三个月之久，

很多病人与他人的话语交流减少了，

心灵深处的活动增多了。

这是濒死的人的一种需要：

离开外在世界，与心灵对话。

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趣拍vlog · 临终关怀公益片，欢迎一起参与到临终关怀这一行动中来。

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趣拍VLOG · 临终关怀公益片，欢迎一起参与到临终关怀这一行动中来。_哔哩哔哩_bilibili

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＃趣拍VLOG＃两周前去上海当了三天义工，拍了一期关于临终关怀的公益短片，目前全国有4000万临终老人，义工数量还远远不够。在等待死亡来临前的每一分每一秒，如果不能用爱和陪伴填满，那必将是一种巨⼤的孤独和煎熬。欢迎一起参与到临终关怀这一行动中来。, 视频播放量 15865、弹幕量 59、点赞数 998、投硬币枚数 622、收藏人数 424、转发人数 201, 视频作者 趣评测, 作者简介 ...

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探讨人口老龄化 如何关怀？/Exploring Population Aging How to Care?

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探讨人口老龄化 如何关怀？/Exploring Population Aging How to Care?

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Chinese-English是[经济学人]老龄化，转危为机。的第1集视频，该合集共计2集，视频收藏或关注UP主，及时了解更多相关视频内容。

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对比日本，10年之后，深度老龄化的中国会是什么样子？_哔哩哔哩_bilibili

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对比日本，10年之后，深度老龄化的中国会是什么样子？

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对比日本，10年之后，深度老龄化的中国会是什么样子？_哔哩哔哩_bilibili

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参考文献：1）估算了中国30年后的人口数据，把我自己给吓着了|财主家的余粮；2）第三代"婴儿潮"消退，40年未见之变局 | 梁中华；3）全球百年房价大趋势 | 任泽平；4）日本房地产大泡沫的催生、疯狂、破灭与教训 | 任泽平5）老龄化意味着什么？从日本低通胀谈起 | 张静静6）老无所养，对于他们已成现实7）谁来为我们养老？| 小熊说保险数据来源：中国统计局 日本统计局 世界银行 中国房产协会Tra, 视频播放量 1837211、弹幕量 40451、点赞数 73419、投硬币枚数 48620、收藏人数 37238、转发人数 19744, 视频作者 路财主, 作者简介 更多内容可关注微信公众号【财主家的余粮】，相关视频：日本的低欲望社会是中国的未来吗？年轻人还有没有机会？【阿Test正经比比】，日本的无退休社会，就是我们这一代的宿命，生存靠贷款、40就自杀，谁来拯救还不起债的韩国年轻人？【奇葩帝国13】，压房价、斩教培、叫停996！为什么国家突然猛下狠手？这事绝不简单【毯叔盘钱】，10年前被网暴的清华教授，精准预测了2022年，中国人已经富裕到了什么程度？你的生活真的很糟吗？，谁将承受中国最严重的老龄化？，2022年人口拐点将至！年轻人越来越少，中国人靠什么来养老？，人口即命运，中国人口会怎样？英国德国俄罗斯日本韩国美国又如何？，年轻人不生孩子，中国未来何去何从？

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你受到帮助的时候你的心里是什么感受？积极或消极？How did you feel when you were helped? Positive or negative?

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你受到帮助的时候你的心里是什么感受？积极或消极？How did you feel when you were helped? Positive or negative?

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你身体不适的时候你愿意告诉身边的人吗？/Are you willing to tell the people around you when you are not feeling well?

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你身体不适的时候你愿意告诉身边的人吗？/Are you willing to tell the people around you when you are not feeling well?

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因为身体的影响导致精神也随之产生问题/Because of the physical effects, mental problems also arise

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因为身体的影响导致精神也随之产生问题/Because of the physical effects, mental problems also arise

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从老年人视角体验（感同身受）Experience from the perspective of the elderly (empathy)

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从老年人视角体验（感同身受）Experience from the perspective of the elderly (empathy)

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日本是一个老龄化严重的国家。而其中独居老年女性超过了四百万，是男性的2倍。

这些老龄化社会衍生出来的种种问题，都给越来越多的孤寡老人留下了一个无解的困局：

孤单寂寞的心理状态，以及应对不了的经济压力。

另一方面，日本女性原本相对较低的社会地位也是重要原因。

于是，主动盗窃和被捕入狱，成了当今日本的老年女性得以摆脱孤独、减轻负担的一种途径。
Japan is a heavily aging country. And there are more than four million of these elderly women living alone, twice as many as men.

All these problems derived from an aging society have left an insurmountable dilemma for the growing number of elderly widows and orphans:

The psychological state of loneliness and isolation, and the economic pressure that cannot be coped with.

On the other hand, the already relatively low social status of Japanese women is also an important reason.

As a result, active theft and imprisonment have become a way for older women in Japan today to escape their loneliness and ease their burden.

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社会给老年人和年轻人带来的压力/Social pressures on older and younger people

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社会给老年人和年轻人带来的压力/Social pressures on older and younger people

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随时随地发现新鲜事！微博带你欣赏世界上每一个精彩瞬间，了解每一个幕后故事。分享你想表达的，让全世界都能听到你的心声！

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由于国情不同，每个老龄化社会都面临着不同的挑战。日本作为世界上人口老龄化最严重的国家（65岁及以上人口，占总人口的28%），就一直在应对一个没有被预见过的问题：老年人犯罪，尤其是一些老年女性。

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Every aging society faces different challenges due to different national conditions. Japan, as the country with the world's most aging population (people aged 65 and older, 28% of the total population), has been dealing with a problem that was not foreseen: crime among the elderly, especially some older women.

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“On October 28, 2022, my mother collapsed alone at home after finishing her webcast class and was found and confirmed dead two days later, yet the murderers are still hiding in the vast network, please help me!
My mom was a history teacher in the third middle school in Xinzheng, Zhengzhou City, Henan Province. Since mid-October, all high schools in Xinzheng have started webcasting their classes, and it was at this time that the online violence that drove my mom to her death had begun. However, with my sister and I away at school and my dad away at work, coupled with the fact that my mom was always intent on hiding her unhappiness at work from us, we all neglected to take care of her emotions. I also hated myself for not being able to detect my mom's emotional abnormalities earlier.
In the end, on the night of October 28, 2022, the murderer intensified, disrupting the live class with various nasty and underhanded methods, ignoring my mother's repeated attempts to discourage and maintain discipline, stimulating my mother again and again through voice abuse, sharing screens, interfering with class projection, etc. My mother eventually got emotional and withdrew from the live class in tears. It was two days later that I heard from my mom again. My mom died of a heart attack and died alone at home. The sad news was too much for my sister and I to take. We didn't want to believe and couldn't accept the fact.
At the end of the class, the students said she was "angry" and "disappeared," but no one really knew what had happened during the time she was "missing. It's not that she didn't want to teach her students anymore, and she wasn't so angry that she didn't return messages or accept apologies, because she had already passed away forever due to a heart attack!
I can't imagine how painful and desperate she was in the last few days, and I blame myself for not being there for her when she was suffering so much. My sister and I hadn't slept for two days and nights, and we couldn't stop crying when we thought of my mom. I'm a junior in college and my sister is a sophomore, and we never thought we'd be without our mom for the rest of our lives after we turned 20.
I don't know if my mom's death will bring a hint of guilt and apology to the scum of these killers, and I don't know if my mom's death will make people realize that the profession of a teacher should be respected by students, and that teachers shouldn't have to suffer the insults and humiliation of students out of thin air! The killer is probably reading this article too, right? I want to tell you, you are not guilty of guilt have not helped, crocodile tears never let people feel pity and sympathy. Our family has already reported the case, the investigation is also underway, do not feel lucky because they are underage, a hundred causes will have consequences, you will certainly be punished severely by the law!
Our family believes that the law will give us an explanation and give our mother an explanation. At the same time I sincerely appeal to everyone please respect the teacher it, do not let the education practitioners feel cold heart! Please help me forward diffusion, thank you very much!”

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随时随地发现新鲜事！微博带你欣赏世界上每一个精彩瞬间，了解每一个幕后故事。分享你想表达的，让全世界都能听到你的心声！

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因为不会使用虚拟网课功能 老师遭受网暴去世/The teacher died of a cyber storm because he could not use the virtual online class function

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因为不会使用虚拟网课功能 老师遭受网暴去世/The teacher died of a cyber storm because he could not use the virtual online class function

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随时随地发现新鲜事！微博带你欣赏世界上每一个精彩瞬间，了解每一个幕后故事。分享你想表达的，让全世界都能听到你的心声！

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信息技术的发展对老年人的限制/Limitations of information technology development for older adults

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信息技术的发展对老年人的限制/Limitations of information technology development for older adults

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重点：对老年人个体的影响+对社会的影响/Focus: Impact on individual older adults + impact on society

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重点：对老年人个体的影响+对社会的影响/Focus: Impact on individual older adults + impact on society

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日本千万老人长寿噩梦：有房有存款有子女，老后却被逼破产...

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会是中国式养老的未来吗？

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https://www.health.org.uk/news-and-comment/featured-content/a-mile-in-my-shoes

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Unfortunately, your browser is too old to work on this website. Please upgrade your browser

A Mile in My Shoes is a giant shoebox, a display of shoes, and a series of unique audio stories from people working in, and using, health and social care.

We invite you to step into someone else's shoes and embark on a mile-long physical, emotional and imaginative journey to see the world through their eyes.

Re-imagined for digital audiences, you can now share in this immersive experience online.

Visit the site

What is A Mile in My Shoes?

Empathy is often neatly summed up as 'putting yourself in someone else's shoes'. It's something people working in health and social care services need to be able to do with the people in their care - thinking about a person's situation, understanding their perspective and providing care that meets their needs. But how often do we empathise with the people providing health and social care?

In 2016, the Health Foundation teamed up with the Empathy Museum to develop a collection of stories from people working in, and using, health and social care in the UK. With each story that was shared, the individual donated a pair of their own shoes to be included in the A Mile in My Shoes collection.

An interactive shoe shop, A Mile in My Shoes invites you to step into someone else's shoes and embark on a mile-long physical, emotional and imaginative journey to see the world through their eyes. This collection of stories showcases the remarkable contribution and challenges faced by those working in, and using, our health and social care system.

Audio recordings of more than 30 people working in, and using, health and social care are available - from a junior doctor to a hospital imam - and visitors are given a pair of shoes and an audio device to enable them to literally walk a mile in someone else's shoes. The collection of audio recordings showcases a range of roles in the sector, including frontline jobs like pharmacist, GP and paramedic, to some less visible positions such as finance manager, HR manager and porter.

It also gives insight into the remarkable contribution and sheer diversity of people and positions that contribute towards the nation's health and health care - from a prison psychiatrist, to a carer to a cardiac physiologist.

You can use the audio player below to listen to snippets of some of the stories.

Why is this important?

Every person has a story.

In the busy and challenging health and social care environment, we sometimes fail to look beyond the role and see the people delivering care every day. We believe storytelling is a powerful way to learn, understand, share and connect with each other.

By sharing these thirty-five stories, we hope to encourage greater understanding and empathy across society for people involved in the NHS and social care, while showcasing their extraordinary contribution and the difference they make to all our lives across the UK.

Hear others' experience about the exhibit

A Mile In My Shoes on the road

The exhibit debuted in June 2016 at the NHS Confederation annual conference in England. Since then we've taken it out and about - from the Houses of Parliament to NHS events. At all events people took part enthusiastically, and told us how inspiring and moving they found the experience.

Having a presence at these health sector events presents opportunities for leaders and decision makers to walk in someone else's shoes while listening to their story, as well as showing their support for a creative initiative that puts the spotlight on people working in our unique health and social care system.

Awards for A Mile in My Shoes

In 2018 A Mile in My Shoes won several DRUM awards, including Best Charity/Not-for-profit campaign, Best social good campaign, Best live or virtual event campaign and Best B2B content marketing campaign, as well as a silver award at the International Content Marketing awards for Best B2B campaign.

Chairwoman of the Branded Content Marketing Association and member of the DRUM awards judging panel, Patricia Weiss, believes that the greatest challenge of the century is to provoke empathy in people. "This is an original, authentic and meaningful project," said Weiss. "A rich territory of experiences that amplify the brand values being fully oriented by what is important to the audience and the society.

"Content and experiences like that represents the perfect intersection between the brand purpose and what is really relevant to people. This is what happens when a brand could represent the society and also be a catalyst of a broader conversation that is more people-oriented and less product-oriented, because great stories and experiences are bigger than products and therefore humanize brands."

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Work with us

We look for talented and passionate individuals as everyone at the Health Foundation has an important role to play.

View current vacancies

The Q community

Q is an initiative connecting people with improvement expertise across the UK.

Find out more

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Unfortunately, your browser is too old to work on this website. Please upgrade your browser A Mile in My Shoes is a giant shoebox, a display of shoes, and a series of unique audio stories from people working in, and using, health and social care.

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Specific points of vulnerability for the older population

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Specific points of vulnerability for the older population

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FEATURE: Putting yourself in the shoes of older vulnerable people is scarier than you think

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FEATURE: Putting yourself in the shoes of older vulnerable people is scarier than you think

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We all know someone with a frail condition such as Parkinson's, arthritis, cataracts or a heart condition - among others - but unless we have been in their shoes, like Bucks Free Press reporter Shruti Sheth Trivedi had the opportunity to do this week, we can never truly know how they feel.

According to health bosses, there are currently three million people in the UK aged over 80, and they say this number is expected to almost double by 2023.

The number of centenarians has also risen by 50 per cent since 2002, according to the figures from Buckinghamshire NHS Trust.

I was very kindly invited by the trust to try out their 'frailty equipment' - equipment which allows you to get an idea of how someone with a certain condition could feel physically - at Stoke Mandeville Hospital this week.

Jo Birrell, the hospital's matron for older people, told me as we get older, one in three of us stand a chance of becoming carers, and often that will be on top of our full-time jobs.

She said: "If you don't have what's called end of life planning, you keep getting bounced into hospital.

"You have negative experiences like we've seen across the country this winter - people waiting in corridors, etc. and it's not often what a lot of our patients want.

"So one of the questions we ask people is what matters most to [them] and people often say they just want to be at home - they are worried about what's going to happen to their life. They don't want to be over treated [in hospital].

"These are questions that society now needs to start to discuss - about what's the best thing to do for our older population, and our own responsibilities for looking after people.

"[Being a carer] will be about having to provide care, but it is also about having those brave discussions with our older relatives."

The frailty kit included a very heavy vest, intended to simulate a heart condition, gloves to show what Parkinson's can feel like, noise cancelling headphones with tinnitus sounds, different glasses to simulate different types of sight loss, including cataracts, and equipment to show what arthritis might feel like.

Jo and another nurse, Katriona Kennedy, started off by putting me in the vest, cataract glasses and tinnitus headphones, and asked me to walk towards the stairs.

That was scary enough as I could hardly see anything - everything was hazy with a yellow-grey tint - and the heavy vest felt like it was getting tighter around my chest.

That coupled with the headphone emitting a constant shrill ringing noise, I was sure I was going to fall before I even got to the steps.

Once I got there, Jo helped me down the stairs and took me outside, where she asked me to cross over to the other side, where the main building was. I could barely see the footpath and almost tripped after missing a step.

We went into the main building, where Jo asked me to buy a copy of the Daily Mail from a shop. When I got inside, I could barely read the titles of the papers and had to hold them right up to my eyes before I could see the name.

The worst were the Parkinson's gloves. Jo turned the impulse up fairly high and I could feel my fingers contorting painfully without having any control over them. At one point my wrists felt like they were going to break.

The experience really put into perspective what older people must go through, especially with frail conditions. Not being able to see or hear properly, and feeling a massive weight on my chest showed me what it must be like for those with chronic conditions - it was a real eye-opener.

Jo said: "About 80 per cent of our older people are living really well without using health and social care.

"So it's a real myth that all older people are a drain on the health service or older people are the problem. It's only about 20 per cent who are using it but they are high users - they use about 80 per cent of resources.

"Those discussions [about end of life care] should really be owned by society not the NHS."

If you need any extra support, or know anyone who does, please contact your GP in the first instance.

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FEATURE: Putting yourself in the shoes of older vulnerable people is scarier than you think

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We all know someone with a frail condition such as Parkinson's, arthritis, cataracts or a heart condition - among others - but unless we have been in their shoes, like Bucks Free Press reporter Shruti Sheth Trivedi had the opportunity to do this week, we can never truly know how they feel.

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Putting yourself in the shoes of the elderly

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Putting yourself in the shoes of the elderly

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PressReader.com - Digital Newspaper & Magazine Subscriptions

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Digital newsstand featuring 7000+ of the world's most popular newspapers & magazines. Enjoy unlimited reading on up to 5 devices with 7-day free trial.

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Goal F: Understand health disparities related to aging and develop strategies to improve the health status of older adults in diverse populations

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Learn about NIA's 2020-2025 research goal to understand health disparities in older adults and work to improve health in these groups.

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We help millions of people to know their rights and make the best choices for later life.

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We help millions of people to know their rights and make the best choices for later life.

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https://www.ageuk.org.uk/services/information-advice/

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Age UK know that growing older doesn't come with a manual. That's why we provide free information and advice to help you on diverse topics.

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https://www.ageuk.org.uk/services/

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Age UK services for the elderly | Age UK

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From befriending and social activities to footcare, information and advice and our charity shops, Age UK delivers a number of services designed to help older people

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Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via G. Amendola 122, 70126 Bari, Italy

Author to whom correspondence should be addressed.

Academic Editors: Susanna Spinsante and Jose Manuel Molina López

Received: 7 April 2021 / Revised: 3 May 2021 / Accepted: 16 May 2021 / Published: 19 May 2021

Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today's society from both an economic and societal standpoint. AAL can provide an arrary of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. A vast amount of literature exists on this topic, so this paper aims to provide a survey of the research and skills related to AAL systems. A comprehensive analysis is presented that addresses the main trends towards the development of AAL systems both from technological and methodological points of view and highlights the main issues that are worthy of further investigation.

1. Introduction

In the last decade, smart environments have found wide application in several contexts, such as domotic, education, rehabilitation, assistance, and so on. Structuring classroom environments with new technologies makes learning more attractive, as well as providing support for teaching people with disabilities [ 1]. Similarly, new technological devices provide more specific tools and support for the rehabilitation of people with impairment [ 2, 3]. In the context of home automation, the deployment of new technologies can improve the quality of daily life, provide easy access to many functions remotely, or allow the use of natural interfaces, such as gestures and voice, in order to control lighting, climate, entertainment systems, and appliances [ 4, 5].

Contextually, a new need has recently emerged: as the population ages, new policies, systems, and technologies for supporting healthy aging are needed [ 6]. Aging leads to gradual physical decay, which increases the dependency on other people. Starting from this consideration, two different aspects can be examined. On the one hand, new technologies can be used to monitor older people in order to reduce daily physical assistance and prolong their autonomous life. In this context, medicine and diet adherence, sleep monitoring, or fall detection are some of the main issues that are to be considered and tackled. On the other hand, monitoring people who are not yet very old and that do not suffer from particular pathologies is strategic for the purpose of detecting eventual lifestyle changes and suspicious behaviors that could warn of the onset of neurodegenerative diseases at a very early stage.

In gerontology literature, the decrease in physical activity is considered to be strictly correlated with ageing, and it is associated with low social function, depression, and cognitive decline [ 7]. A focus on physical, social and cognitive activity is needed in order to promote active and healthy ageing, and to permit people to live independently for longer [ 8]. Active Assisted Living (AAL) technologies can intervene to help elderly people in the different stages of aging. Indeed, AAL is intended to develop innovation in order to keep people connected, healthy, active, and happy into their old age. It concerns the development of products and services that make a real difference to people's lives, for those who face some of the challenges of ageing and those who care for older people who need help.

In recent years, a growing body of literature has examined the state of the art of AAL domain by different points of view. In [ 9, 10], the authors focused on the technologies for Activity Recognition in Smart Home. In [ 11], a review of the literature on mobile technology, such as smartphones, smartwatches, and wristbands, is presented, which describes how these wearable technologies can be used to promote an active lifestyle. Non-wearable (i.e., ambient) technologies are surveyed in [ 12] for the development of various elderly care systems, whereas IoT Wearable Sensors are reviewed in [ 13, 14]. In [ 15], video-based technologies have been reviewed. The AAL system requirements and implementation challenges examining the existing Reference Models and Reference Architectures, from an architectural point of view, are investigated in [ 16], which highlight both the quality attributes and critical issues.

This paper surveys the recent literature on AAL systems from a different perspective, with the aim of providing an exhaustive exploration of contexts, technologies, and existing approaches to figure out the actual needs for developing a complete AAL system. In recent years, as the great technological progress has led to the spread on the market of several kinds of sensors, many papers and research projects have been presented. In this paper, a selection of the more recent published works has been discussed while considering the application contexts, the potential users, and the functionalities of the proposed systems, as well as the technologies and methodologies that are used for their development. The referenced papers have been selected by searching the main bibliographic databases, including IEEE Xplore, ScienceDirect, ACM, and open access journals, and considering those that were published in the previous five years. During the search, some key terms were used, such as "ambient assisted living technology", "elderly", "daily activities", "smart home", "ambient sensors", and "activity monitoring", with both AND and OR connectives. The screening method included studies that more closely met a predefined set of characteristics that are useful for contributing significantly to the discussion carried out in the paper.

The application context, such as indoor/outdoor environments or private/public environments, imposes strict constraints on the type of technologies to be used; invasive or not, with short or long range distance, and so on. Indeed, the use of different sensors can greatly change the parameters that can be observed. In addition, the category of people to be monitored certainly influences the choice of the technology: people with diseases, people with disabilities, healthy people, and so on. The choice of the methodology for data processing is undoubtedly an additional fundamental aspect involved in the development of AAL systems. There exist several methodologies in the literature, having different peculiarities depending on the complexity of the AAL system output. This, in fact, can vary from a simple alarm, alerting when parameters are out of range, to the classification of complex behaviors that were carried out from the observation and correlation of the temporal sequences of parameters. The aim of this review is to provide an overview of the main issues that are related to the development of a complete AAL system, to highlight the choices that need to be made in terms of sensors and methodologies, in order to realize systems that implement different functionalities, with different target users, and in different possible environments.

The rest of this paper is organized, as follows. In Section 2, different application contexts are considered that focused on the main requirements needed for parameter observation. Section 3 illustrates a panoramic survey of the different sensory technologies. Section 4 presents the methodologies for data analysis and decision making. Finally, a critical discussion is provided in Section 5 to determine the main points to address in future research.

2. Application Contexts

The design and development of an AAL system involve different aspects that can be summarized in three principal words: who, where, and what. Target people are persons with different abilities and skills that have to be monitored by the system (who). The application contexts vary from indoor to outdoor environments and, hence, involve different solutions from both the technological and methodological points of view (where). Finally, an AAL system implies a wide range of functionalities that span from simple alerts when dangerous events happen, to more complex behavioral analysis (what). Figure 1 outlines these aspects in a sketch and they are described in the following sections.

2.1. Target Users

AAL systems are principally targeted to elderly adults, aiming maintain continuous support and prolong their autonomous life in an active and healthy way. Medical studies have categorized elderly people into three different classes, depending on their age: those between the ages of 65 and 74 years are classified as youngest-old, those between 75 and 84 years old as middle-old, and those aged over 85 years as oldest-old [ 17]. For all of these classes of people, health practices are aimed at disease management, at alarm alerting, or at behavioral change management. In recent years, several applications for diabetes control, depression treatment, hypertension control, medication adherence, and psychological support have been developed to allow people to live alone while having the possibility of daily control over their health status. Alarm alerting applications can save human life, when critical events, such as falls, prolonged inactivity, or environmental dangers, are detected. The early detection of behavioral changes is necessary before a notable deterioration of the basic activities involved in daily living [ 18]. Scientific studies have pointed out that the activity levels of people drop significantly when they retire. By the early identification of the risk factors of functional decline, this can be prevented in a large portion of the elderly population at the time of retirement by targeting timely interventions and reducing the risks. Furthermore, behavioral change applications include monitoring some dangerous attitudes, such as smoking, calorie intake for diet and exercise, and physical activity levels.

2.2. Indoor Environments

In the literature, most of the researches focus on AAL systems for indoor environments. One of the main application contexts related to medical and public health practices supported by devices that deliver health care services via mobile communication. New systems and methods have been developed for the continuous monitoring of biological, behavioral, or environmental data, delivering interventions, and assessing their outcomes. Through the development of systems collecting data coming from heterogeneous sensors and additional self-reported data, new information regarding physiological, psychological, emotional, and environmental states can be derived.

Indoor environments can be further differentiated in homes [ 19], where people live alone or with a few relatives (see Figure 2) and retirement residences, where more people live together, move in common spaces, perform group or individual activities, and undertake controlled physical activities [ 20] (see Figure 3). Health status evaluation can be carried out by observing people movements, recognizing their actions, evaluating resting periods, monitoring food intake, and so on. Behavioral analysis can be done by detecting anomalies while comparing the actual behavior with the expected one [ 21]. Social activities in a group or interactions with relatives and friends can also be monitored.

2.3. Outdoor Environments

Elderly people spend a number of hours of the day outside the home environment. Recent studies have pointed out the need for seniors to spend time in outdoor environments, as they are motivated to be more active not only physically, but also spiritually and socially [ 22]. Moreover, outdoor habits, such as walking, shopping, meeting other people, and performing physical activities, can greatly help in the prevention of functional decline. If, on one hand, this is widely recognized to be very beneficial for elderly people, on the other hand it inevitably causes new safety concerns. In outdoor environments, elderly people can be exposed to several risks, such as falls or excessive heat or cold. Furthermore, in the case of people having early symptoms of dementia, wandering and becoming confused or lost are common risks. In these scenarios, the main objective of AAL systems is to provide support to elderly people in various aspects, such as in checking the routes, recognizing anomalous behaviors, evaluating motion activities, and so on.

In recent years, several projects have developed different solutions to expand ambient assisted systems from indoor spaces to outdoor and public environments, with the aim of creating different services for improving the wellbeing of people [ 23]. In general, these projects gather data from people observations, store them securely, and, by their analysis, create intervention systems that can be applied for both general or specific monitoring scenarios. These systems can support elderly people by giving them hints and suggestions that are based on the analyzed data acquired daily [ 24, 25] in order to slow the progression of their cognitive and behavioral decline (see Figure 4) [ 26].

3. Technologies

In accordance with the application domains, different technologies can be used for developing AAL systems, starting from simple IoT devices, to more complex sensor networks composed by environmental sensors, intelligent devices, video cameras, and so on. The variety of technologies automatically implies a greater complexity of data, as they can change considerably in terms of size, heterogeneity, and sampling frequency. Data management involves several issues, such as communication protocols, security controls, energy consumption, failure detection, interoperability among multi vendor devices, and so on. The aspects concerning privacy and the protection of sensitive data must consider the legal obligations that may arise whenever a system provides for the collection, storage, and transmission of data whose misuse can compromise people's rights and freedoms. These technical issues are not discussed here, as they fall outside the scope of this review, but their significance is widely recognized for the development of complex AAL architectures, as extensively documented in literature [ 27, 28, 29, 30, 31, 32, 33, 34].

This section examines the fundamental technologies that are used for people monitoring in AAL scenarios. Table 1 resumes the principal technologies applied in various contexts to create different health assistance tasks. The rapid progress of ICT technologies has recently made the discovery and deployment of many multi-functional devices possible. This has widely encouraged the development of effective AAL systems by the combination of ICT technologies and sensor technologies. Many devices, like smart-objects, connected sensors, wearable sensors, smartphones, and smartwatches, have been integrated with non-invasive sensors, such as cameras or infrared sensors, to build systems for people monitoring in both indoor and outdoor applications. As can be observed, the variety of technologies is wide enough to fulfill some particular constraints: to be non-invasive, to be easily acceptable by subjects, and to not affect users in their normal activities. Indeed, as reported above, the potential users of AAL systems are subjects with special needs and individuals having their own relational networks made up of relatives and friends and carrying out even complex activities, in the context of the home and city.

In the following, four principal categories of technologies will be examined. We start from those that can be easily used, such as wearable sensors, but that require user acceptance, and then move on to those that are less invasive, but that require structuring objects or furniture, i.e., smart everyday objects, up to environmental sensors and social assistive robots.

3.1. Wearable Sensors

In recent years, a number of wearable sensors have been miniaturized and made very efficient in terms of energy consumption. Three-axis accelerometers, gyroscopes, and magnetometers are the most commonly used wearable sensors, which are usually worn at the user hip or waist. They have been extensively applied for different purposes, such as to assess the postural stability of a subject [ 35], to detect and classify falls [ 36, 37, 38, 39, 40], or to analyze the gait cycle [ 41]. Moreover, these sensors are embedded in mobile technologies, such as smartphones, smartwatches, and wristbands, which also support the continuous monitoring of biological, behavioral, and environmental data [ 42, 43, 44].

Solutions that are based on passive Radio-Frequency Identification (RFID) technology have been largely used to identify the dynamic position of people moving in indoor environments. Information regarding human motion is carried out when wearable electromagnetic markers (tags) move within an electromagnetic field that is radiated by an interrogating antenna and sensed by the remote receiver or reader [ 45, 46]. This type of system not only monitors movements and location, but also accidental falls [ 47].

An additional technology that has undergone significant developments over the last two decades is Bluetooth Low Energy (BLE) technology, which has paved the way to Bluetooth Smart for wearable devices, which has also solved the limitations related to low broadband connectivity in some particular environments. This technology has been considered for healthcare applications, such as daily physical activity monitoring, health monitoring in cardiac patients, intelligent stethoscope development, emotional level diagnosis [ 48], falls, and location of elderly people [ 49]. When multiple areas of an environment are equipped with BLE scanners and BLE beacons are attached to home residents, the system records residents' activities, which helps to localize and track them [ 50], detect risky areas, and to prevent injuries and dangerous events without intruding on personal privacy [ 51].

The AAL systems examined so far, principally monitor the behavioral status of people. Additional relevant parameters have to be monitored in order to ascertain overall health status of an individual. These are the physiological parameters, which are also known as vital signs, such as heart rate (HR), blood pressure, body temperature, respiratory rate, and blood oxygen saturation. Keeping track of vital bodily functions can be done through different sensors that can be complementary to the aforementioned sensors in order to build complete long-term health monitoring systems in daily-life scenarios. Recent improvements in the performance and cost-effectiveness of microelectro-mechanical systems have opened new possibilities in continuous vital sign monitoring. In particular, ballistocardiography (BCG) and seismocardiography (SCG) are promising techniques for extracting information on cardiac events and phases. They are based on IMU sensors positioned over the subject's sternum for assessing the breathing rate and quality metrics of physical activities [ 52], or for measuring vibrations that are produced by the heart's mechanical activity [ 53]. Smart technology is also used to equip portable objects, such as glasses, in order to evaluate some vital signs, such as heart rate, respiratory rate, regularity of pulse and respiration, the presence and length of apnea events, the temperature distribution on the face, etc. [ 54]. Such methods are very important for healthcare professionals, because they allow for collecting important medical information without the connection of dedicated hardware to the patient, but with a more natural method.

In outdoor contexts, standard mobile devices can be used for increasing the elderly's independence without them having to interact with the technology. By using the built-in sensors of cellphones, normal activities can be discerned from those that are considered not allowed and dangerous for the health of the observed individuals [ 55]. By integrating information that is provided by a GPS and accelerometer with online weather data, it is possible to infer physical and mental health information, such as long exposure to hazardous weather, wandering, or getting lost in order to create safe outdoor spaces [ 56]. Sensor boxes that are interfaced with mobile/phone applications acting as intelligent navigators can make street navigation accessible to impaired people [ 57].

3.2. Smart Everyday Objects

With the recent emergence of Internet of Things (IoT) and miniaturized sensors and processors, everyday objects can be identifiable, and they possess the ability to communicate and interact: in a word they can be smart. In this way, home appliances can communicate with each other or be controlled remotely, providing home residents with new facilities that are able to detect anomalies or assess health issues early, in order to apply prevention policies or trigger actions [ 58, 59, 60]. In AAL contexts, smart every day objects are generally used together with wearable sensors to acquire joint knowledge regarding individual activities and interactions with the objects in the environment [ 61, 62, 63].

Home cooking is one of the basic activities in a daily routine. Monitoring elderly people when they use kitchen appliances is very important in preventing injures and increasing safety. The first signs of cognitive disorders are often easily seen in behaving variations during meal preparation or cooker handling. Some common appliances, such as refrigerators, microwaves, and cooktops, are interconnected with each other to provide assistance in meal preparation tasks [ 64]. Furthermore, specific conditions, such as the presence of objects on cookers, the presence of lids on the pots, and the liquid level in the pots, can be monitored during the cooking process [ 65].

Sensors in furniture, such as doors, beds, chairs, washstand, toilet, and cupboards, allow for the non-obtrusive monitoring of daily living activities, providing presence statistics of users in different spaces of the environment [ 62, 66]. Furthermore, power meters can be used to monitor appliance usage, such as TV set or lamps [ 63], whereas smart pill box devices can be very useful for checking medication intake [ 61].

Many other solutions have been proposed to provide intelligent functionalities to specific objects and monitor some specific issues that are related to the health and safety of elderly people. Cushion sensors, for example, can be used in wheelchairs to track user posture [ 67] or capture the heart rate of a person sitting on it [ 68]. Smart carpets and smart walls can have various functionalities, such as monitoring the walking activity of people, in order to detect falls, measure gait, and count the number of people traversing the environment (socializing activity) [ 69, 70].

3.3. Environmental Sensors

Environmental monitoring sensors are used to detect parameters, such as temperature or air quality, which could have an adverse impact on elderly people [ 71, 72]. They also contribute to monitoring daily life activities [ 73, 74] or localizing people [ 75, 76, 77, 78] and objects around people [ 79]. Unlike wearable sensors and smart object sensors, environmental sensors are placed in the environment without being invasive for people and without structuring or replacing home objects.

Radio-frequency-based systems, through the analysis of the reflections of radio-frequency signals, can be used for monitoring different people's activities, such as sleep monitoring by inferring the subjects' sleep postures [ 80], capturing people's 3D dynamics, learning people's interactions with the objects [ 81], and for detecting changes in movement patterns [ 82]. Microwave sensors can operate through optically opaque materials, such as clothing, as they are not impacted by external visible lighting and scene color, and they may eventually help to sense through fog and smoke. Intelligent metasurface systems transform measured microwave data into images and allow for the recognition of hand signs and vital signs of multiple non-cooperative people [ 83, 84, 85].

Intelligent optical systems are an example of environmental sensory systems that are used for biomedical signal acquisition to reveal rigid and uncontrollable gestures, postural instability, or small tremor, which can be signs of incoming neurological diseases. Low-cost devices, such as Kinect™and Wii™, can be easily installed for monitoring the daily activity of people in order to keep an active and healthy life under observation and to notify caregivers in the case of calls for help [ 2, 86, 87].

3.4. Social Assistive Robots

Social Assistive Robots are less common than wearable or environmental sensors, but they represent an emerging technology that can support and enhance human activities, especially in heavy tasks [ 88]. Social Assistive Robots can be used for different purposes: to overcome elderly inabilities; to relieve nursing staff from time-consuming, non-empathic, and repetitive tasks in retirement house [ 89, 90]; to transport food or medicine carts; for grasping objects [ 20]; for laundry collection; and, delivery services, mail delivery, warehouse logistics, trash logistics, and cleaning material logistics [ 91]. With the spread of these innovative technologies, the service quality can increase and, at the same time, resident and staff satisfaction improve. In this context, it is also important to personalize the human-robot interaction by endowing the robot with human-like social skills (i.e., natural language processing, user emotion estimation, etc.) [ 92, 93].

3.5. Discussion: Pros and Cons of Different Technologies

The recent advancement in technological developments has made a wide variety of sensors available, even at a low cost, such as wearable rings, bracelets, smart watches, and mobile phones, for monitoring many functional parameters that are related to people's health and activities. These sensors have to be worn and they may not be accepted by older people. Less intrusive sensors can be introduced in intelligent dresses and, together with antennas placed in the environment, can provide information for the detection of people. Other more complex sensors for monitoring vital signs are, at the moment, in an experimental phase and, although they have been shown to be effective in experimental tests, they must be miniaturized for being acceptable as wearable devices or have to be used for disease monitoring with a conscious involvement. Environmental sensors that are based on optical or microwave technologies are less invasive for people and they have been demonstrated to be robust for both activity detection and vital sign monitoring. They have to be placed properly in the environment and they may require infrastructure changes for their installation. Smart objects or sensors in furniture can be used to monitor daily life activities and interactions with objects. Additionally, in this case, some of the proposed solutions only require the addition of IoT sensors to the furniture that is already available; in other cases, it is necessary to replace objects with smart ones. In Figure 5, an example of how an elderly people's residence or house could be structured is provided [ 94]: a large number of sensors could be installed at different locations of the environment, depending on the specific parameters that have to be measured and the particular events that would be monitored. It is evident that the selection of sensors among all of these possibilities depend on several factors: the costs of the sensors, their intrusiveness, their acceptability from users, the level of intervention that is required in the environments, the privacy issues that are related to the use of each sensor, and so on. Because these sensors have widely demonstrated their usefulness and potentialities, in the future they are certainly going to become of ordinary use. Thus, not only will the wearable sensors be a part of everyday life, but the environmental ones, even if they require structural interventions, will also be given in the new residences, thus making the flats and all the furniture smart.

4. Methodologies for Data Analysis

Many AAL systems only require data collection and the application of simple techniques for data analysis, such as threshold-based or distance-based methods. These techniques can be enough to trigger alerts when dangerous events are detected, providing interesting supports in various contexts of AAL. They only require a parameter estimation phase to be obtained by statistical analyses of the acquired data. When multiple heterogeneous sensors are used, several types of features have to be analyzed and more complex functionalities have to be recognized. The problem of feature selection becomes relevant for both reducing the cost of AAL systems by facilitating the execution of algorithms on devices with limited resources, as well as for generating reliable classification models of human activities and daily living. Several works have tested feature selection methods on large heterogeneous data comparing the results among different selections of spatio-temporal features [ 95, 96, 97].

The application of Machine Learning (ML) methodologies can solve both the problems of feature selection and data processing in order to automatically build predictive models. ML algorithms, such as Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), Naïve-Bayes (NB), Random Forest (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP), build models of each class through a training phase in a supervised learning style and then classify data in complex situations where the data are not linearly separable [ 98, 99]. SVM and kNN are instance-based methods. They create sets of example data, and new instances of data are compared to them using a similarity measure to find the best match and make a prediction. NB is a Bayesian method that explicitly applies Bayes' Theorem requiring the knowledge of a priori and conditional probabilities that are related to the problem under consideration. DT and RF are tree-based methods that build a model resembling a sort of decision-making diagram that is based on actual values of attributes in the data. A prediction for a given new record is obtained following the tree structure until a leaf is reached. ANN and MLP are artificial neural networks where the structures are inspired by biological neural networks. They are composed of different layers of neurons (nodes) and different activation functions at nodes for mapping the input examples to the output label of a class.

Deep Learning (DL) methodologies, such as Convolutional Neural Networks (CNN) or Long Short-Term Memory Networks (LSTMs), have been successfully applied to many application contexts in recent years, at the same time solving the problem of feature selection and model construction [ 100]. CNNs are mainly used in Computer Vision for visual imagery. When receiving an image as input, a CNN is able to differentiate among aspects or objects without hand-engineered filters, because CNNs have the ability to learn these filters/characteristics during training, successfully capturing spatial and temporal dependencies. LSTM networks are particularly useful for learning sequences that contain longer term patterns of unknown length, due to their ability to maintain long term memory. Furthermore, stacked LSTM networks enable the learning of higher level temporal features, for faster learning with sparser representations [ 101]. The large amount of data needed for the training phases and the requirement of large computational times and resources constitute the main drawback of DL methodologies.

In Table 2, the most commonly applied methodologies are resumed, together with the sensors that are used for data acquisition, the tasks, and the test sets of participants involved in the experimentation and observed activities. The data that are listed in the table have been grouped while considering the tasks mainly investigated in the development of an AAL system, i.e., fall detection, anomaly detection or abnormal behavior detection, and daily activity recognition. As can be seen in Table 2, in each cited paper experiments have been carried out over different test sets (in terms of number of subjects, subject classes, type of actions, and so on). Therefore, even when considering the same task, it is not possible to provide hints on which methodology performs better than others. Nevertheless, the literature works prove that high accuracy rates can be obtained, even when complex data must be managed.

Fall detection is an important issue that is frequently addressed in AAL literature research, as falls represent one of the main threats to the health and independence of elderly people and the main cause of injuries and hospitalization. Therefore, robust methodologies are needed for the timely detection of falls and to avoid false alarms. Various works in literature apply and compare different ML algorithms among the aforementioned ones, obtaining high accuracy rates of fall classification [ 37, 102]. DL approaches have been also applied in this context, implementing the training phase of DL models in the cloud to learn the models of different falls, and only deploying remotely to the edge gateway the test in order to manage the limited resources and provide security and life-cycle service management [ 40, 103].

The detection of anomalous behaviors in users' routines is a key requirement for AAL systems, as it can provide people with remote support for chronic disease prevention. An abundance of of literature works treat this problem by providing effective solutions. Additionally, in this case, ML algorithms have found considerable applications, as they have been applied to learn the "normality" of behaviors, especially on large data recorded over different periods of time and acquired by heterogeneous sensors [ 70, 104, 105, 106, 107, 108]. The obtained models are used to detect possible anomalies in daily behaviors, such as prolonged inactivity periods, a loss of consciousness, inability to stand up, and similar ones that require immediate medical assistance [ 109]. In a similar way, the models of the normal behavior can be specifically learned for each user after an initial training phase being performed on selected data and can be used to find anomalies from the normal conduct of each individual, thus reducing fake alerts from the lack of adherence to general models [ 110].

Different approaches for activity recognition exploit the discrimination capability of the continuous-time duration of activities and inter-occurrence times of observed events for identifying an irregularity in routine behaviors from statistical histories [ 111, 112]. This type of approaches relies on generative stochastic models that address the recognition of sequential activities from the events, the frequency of events, the continuous duration of each activity, and the continuous time between consecutive events.

Activity recognition tasks can be also performed by studying the postures of people over long periods of time. Optical sensors, such as RGB-D cameras, allow for the extraction of three-dimensional (3-D) skeleton data that better characterize the human postures that are involved in the activities to be recognized [ 113]. In these cases, multiple features (such as skeleton joints coordinates, joints distances, angles among consecutive joints, and so on) have to be analyzed in spatio-temporal domains to assess different attitudes of the body. ML algorithms [ 114, 115] or DL techniques [ 116] allow for the manipulation of these complex features, the extraction of significant information, and the construction of complex models that are associated with each activity. The experiments demonstrate that these approaches have good performances and allow for discerning between similar activities.

An additional important class of approaches, which has been applied in limited cases for AAL systems, are those that are based on unsupervised learning techniques. They can be applied for the clustering of large amounts of unlabelled data, grouping user behaviors, and detecting high-level variations without any prior knowledge or training phase [ 117, 118].

Discussion: Pros and Cons of Different Methodologies

This study has identified several clusters of options for data processing in AAL, depending on the complexity of the target. Many examples of successful applications of simple techniques for data analysis, such as threshold-based or distance-based methods, have been reported in the literature for the recognition of many elementary behaviors. Machine learning methodologies, which have been successfully applied in the contexts of pattern recognition, have been borrowed for processing data of functional and healthy parameters for the purpose of more complex behavioral analysis. Many successful examples of deep learning applications have been also reported. The main problem concerned with the use of more sophisticated machine learning is related to the amount of data that are required for the learning phase. Supervised, unsupervised, and deep approaches require large data sets to build their models. However, this large amount of data is not always available in AAL contexts, where systems must be installed and immediately operational. In many cases, it may be necessary to foresee an initial phase of data collection for the tuning of the methodologies before moving on to their actual use.

Another aspect relates to the extraction of significant features on which these systems have to work. In many cases, data may be redundant, and significant features have to be extracted. Machine learning algorithms for data classification or regression require a preliminary feature extraction phase that is fundamental in assuring good performance of the whole analysis. In many cases, data may be redundant or variegated to be easily interpreted: the task of feature extraction can be too hard to be achieved with the supervision of domain experts. However, deep learning approaches have, in some way, solved this problem, as they learn features automatically from data without the need for manual feature extraction. Conversely, they are even more data-demanding than traditional machine learning approaches.

From the analysis of the literature, it has emerged that an absolute and fair comparison among the different approaches presented in different works is not possible. Most of the approaches are applied and tested in limited contexts, such as in labs or selected use cases, and others use data extracted from publically available datasets [ 119, 120, 121]. Accordingly, the reported results are based on different experimental data; therefore, the superiority of one methodology rather than another cannot be argued. However, the previous pros and cons analysis can drive the choice amongst the vast array of approaches. In the case of simple tasks, such as binary or few-classes classification tasks, which could not be solved by simple linear discrimination (threshold operations, distance-based analysis, and so on), but involve straightforward feature engineering, classical machine learning approaches (ANN, SVM, DT, kNN, RF, and so on) are good options. On the contrary, in the case of highly unstructured data related to heterogeneous sensors and having intricate relationships between several interdependent variables, deep learning can be the best solution of complex inference, since it can discover hidden patterns, combine them, and make better decisions in an autonomous data-driven way.

5. Discussion

In a society where the elderly population is constantly increasing, technology can help to improve the quality of life of the elderly and extend their independent living. In recent years, AAL systems have been developed to create better living conditions for older and disabled people, and to support caregivers and medical staff with behavioral and alerting information that can be useful for prevention or timely intervention. Furthermore, when considering the recent pandemic situation, where people have been forced into isolation or require assistance, AAL systems have become essential for communication, safety, and security purposes. These systems can be used to monitor simple daily habits, quantifying, for example, movement or rest periods, verifying the meal or medicine intake, and so on, but also to highlight social components that affect psychological well-being. This is valid for both private homes or retirement homes, where many aspects of the daily activities of residents can be automatically checked, which reduces the costs and increases the monitoring capabilities.

Figure 6 outlines the main steps involved in the development of an AAL system. These steps involve several strictly related choices that start from sensor selection for data acquisition up to functionalities that AAL systems intend to provide. First of all, the sensor selection depends on the costs and invasiveness levels for both people and infrastructure. The choice of the functions to be implemented also imposes constraints on the sensors to be used and the modality of data acquisition. Simple functionalities can be realized with few sensors, even at a low cost. More complex functionalities are needed in order to understand the first elementary behaviors, such as interactions with objects, daily activities, hours of rest, quality of sleep, etc., and then the resulting data have to be collected and evaluated to extract higher level knowledge, such as recognizing changes in habits. Heterogeneous sensors must be used and the data collected over a long period of time. In the same way, the methodologies for data analysis that require learning phases for model building can impose constraints on the quantity of data that has to be acquired. Subsequently, all of these aspects should be considered when complete AAL systems are developed.

The analysis of the extant literature on AAL systems has highlighted some fundamental aspects that require the attention of the scientific community.

The need of a human-centered approach.

From a technological point of view, the main aspect that should be considered is certainly connected to the actual acceptability of sensors by people and, consequently, to the reliability of the acquired data not affected by behavioral conditioning due to the awareness of being observed or having behavior assessed [ 122]. The design of AAL systems has to consider the needs of people preferring a human-centred approach, where the end users are involved and participate in all stages of the design process [ 123]. A long term evaluation as part of a field trials "in the wild", regarding functionality, acceptability, and utility of the AAL systems, in general, has only been studied in a few papers [ 124, 125].

The need of large amount of data from real environments.

Another aspect concerns the experimental tests on which the systems proposed in the literature have been evaluated. Many works propose experiments in labs, use data sets, or are applied in real contexts but with a limited period of observations. If these types of tests ensure that single functionalities work, they are rather limited for more complex behavior analysis. In order to ensure effective validity, real environment situations should be considered, where various parameters can be related to different activities that are actually performed during real scenarios of daily life.

The need of learning the "normal" behavior of each individual.

The evaluation of complex behaviors, such as anomaly detection or change detection, also requires special attention. To detect abnormal changes in any monitoring system, the first step is to build a systematic model from long-term observations of normal activities. Later, the normal pattern is compared with new observations and the deviation is estimated. However, in the context of AAL systems, what behavior can be considered to be "normal"? The concept of normality is not general and it cannot be the same for different subjects. It is closely related to each individual, so it must be learned from the prolonged observation of each person. As stated before, recent technological developments have made it possible to easily collect and store a huge amount of data on people's habits. The main point on which future research must focus is to develop methodologies that are able to process this huge amount of data and create customized models of normal behaviors. These methodologies have to select the fundamental features while discarding irrelevant information and recognize deviations from normality as soon as they arise.

The need of adaptive systems.

An additional aspect is worthy of consideration: AAL systems cannot be closed, as the needs and habits of people change over time as well as the parameters to be observed. The methodologies for data analysis must consider the possibility of differently weighting or customizing some parameters rather than others dynamically. Furthermore, the models of normality must be updated in an adaptive way as people's needs or health change.

The evaluation of processing constraints.

Processing constraints also have to be considered when AAL systems must be devised. Many AAL systems have been devised to provide alarms when dangerous situations are detected. In these cases, hard real time processing of data is necessary for providing prompt interventions, but, at the same time, robust processing techniques are necessary to avoid fake alarms [ 126]. In some approaches, information regarding people's behaviors has to be collected and advice provided. In these cases, soft real time processing is necessary, as data can be collected during the whole day, sent to a central server for the overall evaluation, and then the proper users can be provided with the processing results. On the contrary, long periods of observation are required when changes in habits require detection. In this case, systems have to collect data regarding all aspects of the daily life, and the results can only be evaluated when models of normal behaviors have been estimated. In these cases, offline processing can be done on a large sample of data collected in a cloud computing framework.

The importance of data clustering.

The use of unsupervised approaches is very useful for clustering data and assessing the presence of common behaviors. In the following years, consistent with the predicted expanded deployment of smart objects or environmental sensors in the houses, together with the use of wearable sensors by users, large amounts of data will be available. Unsupervised approaches for data clustering will represent a useful and flexible means to analyze behaviors and extract the habits, as well as the social, behavioral, and functional aspects, of subjects for the purpose of providing medical staff with diagnostic support.

The need of interdisciplinary competences

An additionally important point that is involved in the development of AAL systems is the need of interdisciplinary competences to cover all aspects related to the installation, acceptability, and functionality of an AAL system when considering the interaction levels between users and assistive technologies. Experts in technologies and methodologies for data processing, as well as doctors, geriatricians, and psychologists, must work in interdisciplinary teams to design the overall framework of robust and reliable AAL systems.

In conclusion, the next decade will likely witness a considerable spread of technologies in people's home. Accordingly, huge sets of hard real-time data generated by residents and environment will be available. The main challenge that remains is to provide these systems with intelligence. The scientific community must focus on how to use these data, as well as on developing and incorporating intelligence through machine learning, intelligent decision making, and control methods while taking the state of residents and environments and their privacy into account.

6. Conclusions

The development of AAL systems has been receiving considerable attention from the scientific community in the recent years for several reasons: they can reduce the costs of daily life assistance of elderly people; they can monitor the physical and psychological wellbeing of people that live alone; and, they can detect behavioral changes that could be the sign of early stage of neuro-degenerative diseases.

The main aspects of AAL systems related to health care applications have been surveyed in this paper. This review, without any pretense of being exhaustive, proposes an overview of the main issues and choices that need to be made, while considering the possible application contexts, the available technologies, and methodologies for feature extraction and data processing. In particular, a selection of recent papers has been discussed considering the context in which they have been applied (i.e., indoors or outdoors), the target users (youngest old, middle old, oldest old people), the functional tasks that they realize (disease management, activity monitoring, change detection), the sensors used (wearable, smart objects, environmental sensors), and the methodologies applied for data processing (from simple distance methods to machine learning approaches).

As largely discussed throughout the paper, the recent development of technologies and consequent change of the habits of people, due to the introduction of these new sensors in everyday life, will make a huge quantity of data available. The main point relates to the correct usage of both technologies and data for healthy purposes. While younger generations easily and naturally utilize technologies in daily activities, elderly people could be unwilling and uninterested in using sensors, computers, or new technologies. Hence, the psychosocial factors of human-technology interaction, communication, and usage need to be considered when developing AAL systems. These factors, together with data usage and processing, are worthy of further investigations by the scientific community in order to develop increasingly complex intelligent systems for care services, remote consultation, independent living support, social participation and well-being, healthy condition monitoring, and so on.

Author Contributions

Conceptualization, T.D. and G.C.; Investigation, A.P.; Data curation, G.C., A.P., A.M., R.M.; Writing-original draft preparation, T.D. and G.C.; Writing-review and editing, A.M., R.M. and G.C. Visualization, G.C. Supervision, T.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by POR Puglia FESR - FSE 2014-2020. InnoNetwork Call, within BESIDE project (no. YJTGRA7).

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

The authors would like to thank G. Bono and M. Attolico for their administrative and technical support.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:

AAL

Actice and Assistive Living

IoT

Internet of Things

ICT

Information and Communications Technology

IMU

Inertial Measurement Units

RFID

Radio Frequency IDentification

BLE

Bluetooth Low Energy

GPS

Global Positioning Systems

HR

Heart Rate

BCG

Ballistocardiography

SCG

Seismocardiography

ML

Machine Learning

SVM

Support Vector Machine

kNN

K-Nearest Neighbor

ANN

Artificial Neural Network

NB

Naïve-Bayes

RF

Random Forest

DT

Decision Tree

MLP

Multi-Layer Percepron

DL

Deep Learning

CNN

Convolutional Neural Network

LSTM

Long Short-Term Memory Network

RNN

Recurrent Neural Network

GRU

Gated Recurrent Unit

HMM

Hidden Markov Model

CT-HSMM

Continuous-Time Hidden Semi-Markov Model

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Figure 2. Several types of sensors deployed into the house and attached to devices permit gathering a variety of data concerning the location of the resident(s), the object(s) they communicate with, and data related to health conditions (from [ 19]).

Figure 3. A retirement home (from [ 20]): heterogeneous sensors can collect data to support the caregivers in their work, while, at the same time, collect valuable data for the indoor localization of both residents and caregivers. The system detects a fallen person through the signal sent by the ( a, b) Wi-fi bracelet and alerts ( c) the caregiver. The ( d) robot assists a ( e) bedridden resident requesting help.

Figure 4. The solution proposed in [ 26] for activity monitoring in multiple domains of preventive measures: nutritional guidance, physical exercise promotion, cognitive practice, social activity, and positive care planning.

Figure 5. An example of AAL elderly people's residence or house full of sensors, such as a presence sensor or temperature sensor, and actuators, such as light control, home automation control, medication control, and so on (from [ 94]).

Sensor Type

Technology

Context

Task

Ref.

Smart objects

Sensors in appliances and furniture

Indoor

Daily life Activities, Abnormal behavior detection, Interaction with devices

[ 59, 60, 61, 62, 63]

Smart objects

Single smart object (Cushion, wheelchair, carpet, bed)

Indoor

Specific health functionalities, sleeping posture recognition

[ 58, 67, 68, 69]

Environmental

Sensors in the environment

Indoor

Multiple People Location

[ 75, 76, 77, 78]

Methodology

Features

Task

Sensors

Test Set

Ref.

LSTM, GRU, SVM and kNN

Time series of accelerometer data

Fall detection

Wearable sensor: tri-axial accelerometer

23 adults and 15 elders performing several daily activities and falls

[ 40, 103]

# data[].attributes.attachment_link.title
Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population

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Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today's society from both an economic and societal standpoint.

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在过去的十年中，智能环境在多个领域得到了

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在过去的十年中，智能环境在多个领域得到了广泛的应用，例如家庭、教育、康复、援助等。用新技术构建课堂环境使学习更具吸引力，并为残障人士的教学提供支持 [ 1 ]。同样，新技术设备为残障人士的康复提供更具体的工具和支持 [ 2 , 3 ]。在家庭自动化的背景下，新技术的部署可以提高日常生活质量，提供远程访问许多功能的方便，或者允许使用自然界面，如手势和语音，以控制照明、气候、娱乐系统和电器 [ 4, 5 ]。

从上下文来看，最近出现了一个新需求：随着人口老龄化，需要新的政策、系统和技术来支持健康老龄化[ 6]。衰老会导致身体逐渐衰退，从而增加对他人的依赖。从这个考虑出发，可以检查两个不同的方面。一方面，新技术可用于监测老年人，以减少日常的身体帮助，延长他们的自主生活。在这种情况下，药物和饮食依从性、睡眠监测或跌倒检测是需要考虑和解决的一些主要问题。另一方面，监测还不太老且没有患有特定病症的人是战略性的，目的是检测最终的生活方式变化和可疑行为，这些行为可能在很早的阶段就警告神经退行性疾病的发作。

在老年学文献中，体力活动的减少被认为与衰老密切相关，并且与社会功能低下、抑郁和认知能力下降有关[ 7 ]。需要关注身体、社交和认知活动，以促进积极和健康的老龄化，并使人们能够独立生活更长时间[ 8]。主动辅助生活 (AAL) 技术可以干预以帮助处于不同衰老阶段的老年人。事实上，AAL 旨在发展创新，以使人们在晚年时保持联系、健康、活跃和快乐。它涉及对人们的生活产生真正影响的产品和服务的开发，为那些面临老龄化挑战的人以及那些照顾需要帮助的老年人的人。

近年来，越来越多的文献从不同的角度审视了 AAL 领域的最新技术。在 [ 9 , 10 ] 中，作者专注于智能家居中的活动识别技术。在 [ 11 ] 中，对智能手机、智能手表和腕带等移动技术的文献进行了回顾，描述了这些可穿戴技术如何用于促进积极的生活方式。[ 12 ] 对非可穿戴（即环境）技术进行了调查，以开发各种老年护理系统，而物联网可穿戴传感器在 [ 13、14 ]中进行了回顾。在 [ 15]，基于视频的技术已经过审查。在 [ 16 ]中研究了从架构的角度检查现有参考模型和参考架构的 AAL 系统要求和实施挑战，其中突出了质量属性和关键问题。

本文从不同的角度调查了有关 AAL 系统的最新文献，旨在对环境、技术和现有方法进行详尽的探索，以找出开发完整 AAL 系统的实际需求。近年来，随着技术的巨大进步，导致多种传感器在市场上的普及，许多论文和研究项目被提出。在本文中，讨论了一些最近发表的作品，同时考虑了应用环境、潜在用户和建议系统的功能，以及用于开发的技术和方法。参考论文是通过搜索主要书目数据库选择的，包括 IEEE Xplore、ScienceDirect、ACM、和开放获取期刊，并考虑过去五年出版的期刊。在搜索过程中，使用了一些关键术语，例如“环境辅助生活技术”、“老年人”、“日常活动”、“智能家居”、“环境传感器”和“活动监测”，并带有 AND 和 OR 连接词. 筛选方法包括更接近于一组预定义的特征的研究，这些特征有助于显着促进论文中进行的讨论。
In the last decade, smart environments have found wide application in several contexts, such as domotic, education, rehabilitation, assistance, and so on. Structuring classroom environments with new technologies makes learning more attractive, as well as providing support for teaching people with disabilities [1]. Similarly, new technological devices provide more specific tools and support for the rehabilitation of people with impairment [2,3]. In the context of home automation, the deployment of new technologies can improve the quality of daily life, provide easy access to many functions remotely, or allow the use of natural interfaces, such as gestures and voice, in order to control lighting, climate, entertainment systems, and appliances [4,5].

Contextually, a new need has recently emerged: as the population ages, new policies, systems, and technologies for supporting healthy aging are needed [6]. Aging leads to gradual physical decay, which increases the dependency on other people. Starting from this consideration, two different aspects can be examined. On the one hand, new technologies can be used to monitor older people in order to reduce daily physical assistance and prolong their autonomous life. In this context, medicine and diet adherence, sleep monitoring, or fall detection are some of the main issues that are to be considered and tackled. On the other hand, monitoring people who are not yet very old and that do not suffer from particular pathologies is strategic for the purpose of detecting eventual lifestyle changes and suspicious behaviors that could warn of the onset of neurodegenerative diseases at a very early stage.

In gerontology literature, the decrease in physical activity is considered to be strictly correlated with ageing, and it is associated with low social function, depression, and cognitive decline [7]. A focus on physical, social and cognitive activity is needed in order to promote active and healthy ageing, and to permit people to live independently for longer [8]. Active Assisted Living (AAL) technologies can intervene to help elderly people in the different stages of aging. Indeed, AAL is intended to develop innovation in order to keep people connected, healthy, active, and happy into their old age. It concerns the development of products and services that make a real difference to people’s lives, for those who face some of the challenges of ageing and those who care for older people who need help.

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年龄中位数较低的国家往往人口增长率较高/Countries with a lower median age tend to have higher population growth rates

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年龄中位数较低的国家往往人口增长率较高/Countries with a lower median age tend to have higher population growth rates

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老年人的情感记忆 需要朴素感人<

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老年人的情感记忆 需要朴素感人

人文的 道德的 心灵的关怀

生理需求 社会需求 文化需求
The emotional memory of the elderly needs to be simple and touching

Humane Ethical Heartfelt care

Physiological needs Social needs Cultural needs

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Launch of Qatar Pailliative care needs assessment for old persons

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Launch of Qatar Pailliative care needs assessment for old persons

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https://thewhpca.org/2020-08-05-08-22-47/latest-news/item/launch-of-qatar-palliative-care-needs-assessment-for-older-persons

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The Worldwide Hospice Palliative Care Alliance

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Today a new palliative care needs assessment for older persons in Qatar was launched at the World Innovation Summit on Health in Doha. The report addresses many attitudes about death and dying in an important middle eastern country. The great majority of Qataris want to know their diagnosis, prognosis and treatment options. Download the report from our resources section at: https://www.thewhpca.org/resources/item/qatar-pc-needs-assessment-older-persons

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Launch of Qatar Palliative Care Needs Assessment for Older Persons

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Today a new palliative care needs assessment for older persons in Qatar was launched at the World Innovation Summit on Health in Doha. The report addresses many attitudes about death and dying in an important middle eastern country. The great majority of Qataris want to know their diagnosis, prognosis and treatment options.

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Women can expect to live longer than men, with life expectancy at birth in the UK being 78.1 years for men and 82.1 years for women in 2010.4 However, women are also more likely to live more years in poor health. In 2010, the expected years lived in poor health from age 65 onwards was 7.7 years for men and 8.7 years for women.5 Family members supply the majority of social care provided in the community. In 2001, over three-quarters (78 per cent) of all older people who reported suffering from mobility problems were helped by their spouse or other household members. As well as receiving informal care, older people are also major providers of care. In 2001, 1.2 million men and 1.6 million women aged 50 and over in England and Wales were providing unpaid care to family members, neighbours or relatives. This represents 16 per cent and 17 per cent of men and women aged 50 and over. Among 50- to 64-year-olds, a greater proportion of women than men provide unpaid care, and a higher proportion provide intensive care (50 or more hours a week). 6

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承认老龄化社会的多样性 有多种不同的老龄人的诉求/Recognizing the Diversity of an Aging Society There is a Diversity of Aging Claims

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承认老龄化社会的多样性 有多种不同的老龄人的诉求/Recognizing the Diversity of an Aging Society There is a Diversity of Aging Claims

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心理的需求（陪伴 关怀）
记忆的丧失
身体的疼痛
社会的快速科技化发展与老龄人群的格格不入（老年人不会使用社交媒体 购物 出行的不便）
Psychological needs (companionship Care)
Memory loss
Physical pain
Rapid technological development of society and the incompatibility of the aging population (seniors don't use social media shopping inconvenience of travel)

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老年人面对死亡的自述与哲思/Self-reports and philosophies of elderly people facing death

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老年人面对死亡的自述与哲思/Self-reports and philosophies of elderly people facing death

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一位97岁哲学家如何面对他的死亡？_哔哩哔哩_bilibili

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一位97岁哲学家如何面对他的死亡？共计3条视频，包括：中英字幕、英文字幕、无字幕等，UP主更多精彩视频，请关注UP账号。

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生理需求
安全需求
社交需求
尊重需求
自我实现需求
Physiological needs
Security Needs
Social needs
Respect needs
Self-actualization needs

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全球年龄结构的过去未来/The Past Future of the Global Age Structure

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全球年龄结构的过去未来/The Past Future of the Global Age Structure

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1950年，地球上有25亿人。现在到 2022 年，大约有 80 亿。到本世纪末，联合国预计全球人口将达到约104亿。人口金字塔的这种可视化使得理解这一巨大的全球转变成为可能。

人口金字塔可视化了人口的人口结构。宽度表示给定年龄的人口规模;右边是女人，左边是男人。底层代表新生儿的数量，在其上方，您可以找到年龄较大的队列的数量。以这种方式表示，高死亡率社会的人口结构类似于金字塔——这就是这种著名的可视化类型得名的原因。

在最深的蓝色中，您可以看到代表 1950 年世界人口结构的金字塔。两个因素导致了1950年的金字塔形状：越来越多的出生扩大了人口金字塔的底层，金字塔向顶部变窄，很明显，整个生命的死亡风险持续很高。相对于老年人的数量，新生儿数量很多。

金字塔在底部上方的变窄证明了这样一个事实，即1950年出生的儿童中有五分之一以上在五岁之前死亡。¹

通过蓝色和绿色的阴影，相同的可视化显示了截至2018年的过去几十年的人口结构。你可以看到，在随后的每个十年中，人口金字塔都比以前更大 - 在每个十年中，世界人口中增加了更多各个年龄段的人。

如果你看一下2018年的绿色金字塔，你会发现底部上方的变窄远不如1950年那么强烈;儿童死亡率从1950年的五分之一下降到今天的不到十分之一。

在比较1950年和2018年时，我们看到出生儿童人数有所增加——1950年为9700万，今天为1.43亿——同时儿童死亡率下降。如果你现在将2018年的金字塔底部与2100年的预测进行比较，你会发现未来几十年不会像过去一样：根据预测，本世纪末出生的孩子将比今天少。未来人口结构的基础更窄。

我们正处于全球人口历史的转折点。从1950年到今天，整个金字塔的扩大 - 儿童数量的增加 - 是世界人口增长的原因。从现在开始，不是基数的扩大，而是基数以上人口的“填充”：儿童数量将几乎没有增加，然后开始下降，但工作年龄和老年人的数量将大幅增加。随着全球健康状况的改善和死亡率的下降，预计今天活着的人将比我们之前的任何一代人活得更长。

在国家一级，“儿童高峰期”之后往往伴随着国家受益于“人口红利”的时期，即受抚养的年轻一代的比例下降，劳动适龄人口的比例增加。³

现在，这种情况正在全球范围内发生。1950年，每一名15岁以下的儿童就有1.7名工作适龄人口（15至64岁）;今天有 2.6;到本世纪末，将有3.6。⁴

富裕国家在过去几十年中从这一转变中受益，现在正面临着越来越多的退休人员不属于劳动力市场的人口问题。在未来几十年里，较贫穷的国家将受益于这一人口红利。

从1950年到今天的变化和到2100年的预测表明，世界人口正在变得更加健康。当金字塔的顶部变得更宽，看起来不像金字塔，而是变得更像盒子形时，人口会活到更年轻的年龄，死亡风险非常低，并在老年时死亡。在人口转型的最后阶段，健康人口的人口结构是我们在2100年看到的整个世界的盒子形状。

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关于如何进行老龄化设计伦理的价值判断/Value judgments on how to conduct design ethics for aging

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关于如何进行老龄化设计伦理的价值判断/Value judgments on how to conduct design ethics for aging

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关注老年人的真实需求进行设计/Design with a focus on the real needs of the elderly

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从老年人的角度看这个社会/Looking at the society from the perspective of the elderly

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从老年人的角度看这个社会/Looking at the society from the perspective of the elderly

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老龄化的社会福利问题/Social welfare aspects of aging

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老龄化的社会福利问题/Social welfare aspects of aging

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年轻人的工作压力更大 负担家庭和社会的责任增多 养老金需要年轻人承担/Young people are working under more pressure and taking on more responsibility for family and society Pensions need to be borne by young people

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年轻人的工作压力更大 负担家庭和社会的责任增多 养老金需要年轻人承担/Young people are working under more pressure and taking on more responsibility for family and society Pensions need to be borne by young people

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不同地域的人口老龄化/the ageing situation in different areas

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不同地域的人口老龄化/the ageing situation in different areas

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south asia
Africa
Europe
America

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劳动力短缺、资本短缺导致经济增长放缓/Labor shortage, capital shortage lead to slowing economic growth

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劳动力短缺、资本短缺导致经济增长放缓/Labor shortage, capital shortage lead to slowing economic growth

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老年人清算资产，花费储蓄，从而导致资产市场不景气，储蓄率降低/Older people liquidate their assets and spend their savings, thus leading to a depressed asset market and lower savings rates

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老年人清算资产，花费储蓄，从而导致资产市场不景气，储蓄率降低/Older people liquidate their assets and spend their savings, thus leading to a depressed asset market and lower savings rates

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退休人员比新入职的劳动力数量要多，从而导致劳动力短缺/There are more retirees than new entrants to the workforce, resulting in a labor shortage

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退休人员比新入职的劳动力数量要多，从而导致劳动力短缺/There are more retirees than new entrants to the workforce, resulting in a labor shortage

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人口老龄化所引发的社会问题/Social issues arising from the aging of the population

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人口老龄化所引发的社会问题/Social issues arising from the aging of the population

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2020年全球主要经济体人口老龄化排名/Global ranking of major economies in terms of population aging in 2020

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2020年全球主要经济体人口老龄化排名/Global ranking of major economies in terms of population aging in 2020

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数据来源：CSMAR人口老龄化-各国家及地区人口年龄构成情况表（世界银行）
Data source: CSMAR Population Aging - Age Composition of Population by Country and Region (World Bank)