Revisiting the Technology Challenges and Proposing Enhancements in Ambient Assisted Living for the Elderly

Abstract

Several social and technical trends support the elderly's desire to live independently in their preferred environment, despite their increasing medical needs, and enhance their quality of life at home. Ambientassisted living (AAL) has the capabilities to support the elderly and to decrease their dependency on formal or informal caregivers. We provide a review of the technological challenges that were identified as inhibiting factors in the past decade and then present recent technological advances, e.g., cloud computing, machine learning, artificial intelligence, the Internet of Things. We also fill the gap in the current literature in regard to specific AAL solutions and propose fourthgeneration AAL technology design. We find that most informal caregivers are family members who are medically untrained and that the use of advanced analytical processes on AAL-generated data could significantly increase symptom identification. We also present the implications and remaining challenges along with recommendations for future research.

Variational autoencoders for anomaly detection in the behaviour of the elderly using electricity consumption data

“…On the one hand, restrictive systems (e.g., door locking or medication), which lead to a loss of independence of older people. On the other hand, monitoring systems using some form of sensorisation (Bozan & Berger, 2019;Ghayvat et al, 2018;Monekosso et al, 2015). These scenarios represent intelligent environments capable of unobtrusively recognizing the presence of individuals and seamlessly reacting to them.…”

Context-aware Plan Repair in Environments shared by Multiple Agents

“…A variation in stress levels directly affects the emotional state and negatively influences physical and mental health. A recent study (Bozan et al 2019) shows that negative emotions are the top problem-related category for AL. Stress is detected through a variety of bio-signals such as electroencephalography (EEG), photoplethysmography (PPG) and electrocardiogram (ECG), and facial expressions analysis, which is contactless, allowing detecting a larger spectrum of emotions (Gradl et al 2019).…”

“…AmI has proven helpful for healthcare and senior citizens. It has been used in a broad spectrum of related applications, such as monitoring seniors and alerting caregivers, recognising seniors' activities, Alzheimer detection, managing stress, and empowering people living with the early stages of dementia with autonomy (Bozan et al 2019;Schmidt et al 2018;Giménez-Manuel et al 2022).…”