Assisted Living of the Elderly: Recent Advances, Systems, and Frameworks

Dear Colleagues,

Increased longevity is now more commonplace than ever before, with the global average life expectancy reaching 60 years or above. This is mostly due to medical breakthroughs and advances in healthcare research [1]. The world’s population over the age of 65 is growing dramatically, numbering 962 million today, and it is projected to increase to 2 billion by 2050 [2,3]. As the population ages, modern society is facing a wide range of difficulties stemming from numerous conditions associated with the elderly, such as varying rates of decline in behavioral, social, emotional, mental, psychological, and motor abilities, as well as other issues such as cognitive impairment, behavioral disorders, disabilities, neurological disorders, dementia, Alzheimer’s disease, and visual impairments, which are associated with the process of aging [4]. Over the last few years, aging populations across the globe have had to contend with a decrease in caregivers to care for them, which has created a variety of challenges and difficulties [5–7]. Two major problems in this context are as follows. Firstly, as the demand has increased, the cost of caregiving has risen considerably in recent years. As a result, affording caregivers is becoming increasingly difficult. Secondly, quite often, caregivers take care of multiple elderly people with multiple varying needs during the day; as a result, they are frequently exhausted, overworked, overwhelmed, and overburdened, which affects the quality of care.

This Special Issue invites papers presenting new discoveries, theoretical findings, practical solutions, use cases, analytical findings, and novel applications that specifically focus on assisted living for the elderly population. Potential topics include, but are not limited to:

• Ambient assisted living in smart homes;
• Fall detection;
• Frameworks for the detection of cognitive impairment;
• Systems of safe navigation for the visually impaired;
• Indoor localization;
• Affect-aware systems;
• Human behavior recognition and analysis;
• Robot-assisted therapy;
• Socially assistive robots.

Authors are invited to contribute their original and unpublished works. We welcome research and review papers alike. Research papers presenting preliminary and proof-of-concept results are also welcome. Authors may also submit extended versions of their conference papers. However, the authors of such papers should make significant improvements/extensions to their conference paper, and the details of these improvements/extensions should be clearly outlined in the cover letter accompanying the paper’s submission.

References:

1. Zhavoronkov, A.; Bischof, E.; Lee, K.-F. Artificial Intelligence in Longevity Medicine. Aging 2021, 1, 5–7. https://doi.org/10.1038/s43587-020-00020-4.
2. Decade of Healthy Ageing (2021–2030). Available online: https://www.who.int/initiatives/decade-of-healthy-ageing (accessed on 9 May 2022).
3. Ageing and Health. Available online: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (accessed on 9 May 2022).
4. Remillard, E.; Campbell, M.L.; Koon, L.M.; Rogers, W.A. Transportation Challenges for Persons Aging with Mobility Disability: Qualitative Insights and Policy Implications. Disabil. Health J. 2022, 15, 101209. https://doi.org/10.1016/j.dhjo.2021.101209.
5. Wu, C.H.; Lam, C.H.Y.; Xhafa, F.; Tang, V.; Ip, W.H. The Vision of the Healthcare Industry for Supporting the Aging Population. In Lecture Notes on Data Engineering and Communications Technologies; Springer International Publishing: Cham, Switzerland, 2022; pp. 5–15, ISBN 9783030933869.
6. Yang, W.; Wu, B.; Tan, S.Y.; Li, B.; Lou, V.W.Q.; Chen, Z.A.; Chen, X.; Fletcher, J.R.; Carrino, L.; Hu, B.; et al. Understanding Health and Social Challenges for Aging and Long-Term Care in China. Res. Aging 2021, 43, 127–135. https://doi.org/10.1177/0164027520938764.
7. Javed, A.R.; Fahad, L.G.; Farhan, A.A.; Abbas, S.; Srivastava, G.; Parizi, R.M.; Khan, M.S. Automated Cognitive Health Assessment in Smart Homes Using Machine Learning. Sustain. Cities Soc. 2021, 65, 102572. https://doi.org/10.1016/j.scs.2020.102572.

Dr. Nirmalya Thakur
Guest Editor

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• assisted living
• elderly population
• artificial intelligence
• machine learning
• data science
• data analysis