Abstract
The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.
Highlights
According to the World Health Organization (WHO) the proportion of people over 60 years will double from 11% in 2006 to 22% by 2050 [1]
A survey of smart environments and robot assistive technologies that have the potential of supporting the independent living of older adults at home by implementing age-friendly care services. In this process we identify the challenges in implementing the new care service models, existing technology limitations and its acceptance by the older adults; A discussion on how these technologies are used for the development of two care services for older adults centered and integrated care polypharmacy management and control of wellbeing decline by social and cognitive activity engagement
The results presented in that article show that the deep learning methods such as long short-term memory (LSTM) and gate recurrent unit (GRU) are better than the classical machine learning methods such as support vector machines (SVM) and k-nearest neighbors (K-NN) for falls detection
Summary
According to the World Health Organization (WHO) the proportion of people over 60 years will double from 11% in 2006 to 22% by 2050 [1]. The combination of activity, safety, social and comfort functionality can lead to an extension of the time spent living in their own home by shifting or postponing parts of in-patient (i.e., care facility services) treatment, therapy and exercise training into the own home; thereby it will directly decrease or delay admissions into a care institution, diminishing the use of professionalized care services and/or lowering the burden of health care services and facilities In this context, the smart and ‘age-friendly’ environments will become fundamental pervasive technologies for supporting older adults care while the social robots may become important intervention tools due to their ability to provide support to older persons and open links with their community and professional caregivers and to cooperate and interact with older adults engaging them in the care process. The study is structured as follows: Section 2 reviews existing smart environments and social robots’ technologies for older adult care, Section 3 analyses the application of the technology for new integrated care services and Section 4 presents the conclusions of the review
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