Enhancing home rehabilitation through AI-driven virtual assistants: a narrative review
David B Olawade1,2,3, Khadijat K Adeleye4, Eghosasere Egbon5
1Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK.
Background And Objective:
Artificial intelligence (AI)-driven virtual physiotherapy assistants (VPAs) are increasingly adopted in home-based rehabilitation, offering real-time feedback and personalised guidance through wearable sensors. These systems enhance treatment adherence, minimise clinic visits, and improve rehabilitation outcomes. However, challenges such as sensor accuracy, patient engagement, and affordability hinder widespread implementation. This review explores current applications, benefits, and limitations of AI-driven VPAs.
Methods:
A comprehensive narrative review was conducted across PubMed, IEEE Xplore, Scopus, Google Scholar, and Web of Science databases. Search terms such as: "artificial intelligence", "virtual physiotherapy assistants", "home rehabilitation", and "wearable sensors". From 847 initially identified articles, 31 peer-reviewed publications (2018-2024) met inclusion criteria. Exclusion criteria eliminated non-English publications, conference abstracts, and studies without AI components. The review synthesised literature on sensor accuracy, AI-based monitoring algorithms, and patient engagement strategies.
Key Content And Findings:
Analysis of 31 studies revealed that AI-driven VPAs enhance adherence and reduce in-person visits. Integrating wearable sensors and AI facilitates real-time feedback and personalised support, improving exercise accuracy. Critical limitations include inertial measurement unit drift, electromyography sensor placement variability, and optical system environmental dependencies. Challenges remain in sensor precision, user motivation, cost barriers, and technology accessibility. Novel findings highlight potential for predictive analytics, gamification strategies, and telehealth integration.
Conclusions:
AI-driven VPAs offer a promising accessible, personalised home-based rehabilitation solution. Evidence demonstrates therapeutic potential, though systematic addressing of sensor accuracy, engagement strategies, and accessibility barriers is essential for implementation. Technological improvements and increased affordability are crucial for broader adoption and long-term impact on rehabilitation delivery.
