A Review and Comparative Analysis of Context-Aware Frameworks for Detecting Abnormal Activities in Daily Living for Elderly

The global population is going through a significant demographic shift, where the percentage of individuals aged 65 and over is projected to increase globally. This demographic transition brings forth challenges associated with aging, including an increased prevalence of non-communicable diseases and disabilities. Older individuals commonly face health issues such as hearing loss, cataracts, chronic diseases, and mental health concerns. There is a growing need for comprehensive care and support systems encompassing primary, acute, and end-of-life care, along with assistance in daily activities. To address these complexities, researchers focus on enhancing the quality of life for the elderly by analyzing Activities of Daily Living (ADLs) through context-aware frameworks. However, a critical review and comparison of these frameworks remains a relatively unexplored area. To address this gap, this paper conducts a comparative analysis of existing context-aware frameworks for Abnormal Activities of Daily Living (AADL) detection, whereby identifying key limitations and proposing directions for future development. In this study, 6 such frameworks have been reviewed and compared and results revealed the limited evaluation of existing frameworks in relation to AADL detection capabilities.