This feasibility study explores the development of the HomeAI-Enable system, an AI-driven decision support platform designed to provide personalised housing adaptations and assistive technology recommendations for older adults and individuals with disabilities. The study follows a constructive research approach including focus groups and brainstorming exercises in addition to development research techniques. Three different datasets comprising one, five, and over 500 diseases sourced from reputable databases such as the NHS and Mayo Clinic were used. The datasets include symptoms, diseases and associated housing adaptation and assistive technologies for the identified symptoms and diseases. The system integrates these datasets to offer personalised solutions based on symptoms, disease, body functions, and environmental factors, using advanced techniques like fuzzy matching, natural language processing, and advanced machine learning models. The feasibility study involves the assessment of multiple AI models to determine their effectiveness in providing tailored recommendations for assistive technology and housing adaptations. A proof of concept was developed, and early technical evaluation demonstrates the viability of the proposed architecture and recommendation workflow for housing adaptation decision support. This study evaluates the technical feasibility of the system at a foundational stage within a broader research programme, with clinical validation involving occupational therapists and end users planned for subsequent phases.
Saleela et al. (Sat,) studied this question.