A situation-driven framework for relearning of activities of daily living in smart home environments

Activities of Daily Living (ADLs) are sine qua non for self-care and improved quality of life. Self-efficacy is major challenge for seniors with early-stage dementia (ED) when performing daily living activities. ED causes deterioration of cognitive functions and thus impacts aging adults’ functioning initiative and performance of instrumental activities of daily living (IADLs). Generally, IADLs requires certain skills in both planning and execution and may involve sequence of steps for aging adults to accomplish their goals. These intricate procedures in IADLs potentially predispose older adults to safety-critical situations with life-threatening consequences. A safety-critical situation is a state or event that potentially constitutes a risk with life-threatening injuries or accidents. To address this problem, a situation-driven framework for relearning of daily living activities in smart home environment is proposed. The framework is composed of three (3) major units namely: a) goal inference unit – leverages a deep learning model to infer human goal in a smart home, b) situation-context generator – responsible for risk mitigation in IADLs, and c) a recommendation unit – to support decision making of aging adults in safety-critical situations. The proposed framework was validated against IADLs dataset collected from a smart home research prototype and the results obtained are promising