Artificial Intelligence Techniques in Enhancing Home-Based Rehabilitation: A Survey

Document Type : Original Article

Authors

1 HCI-LAB, FCAI Helwan University Faculty of Computer Science October University for Modern Sciences and Arts (MSA)

2 Computer Science Dept. , faculty of computer and Aritifal intelligence Helwan university

3 Information Systems Dept. , faculty of computer and Aritifal intelligence Helwan university

Abstract

Globally, an estimated 2.4 billion people live with health conditions that may benefit from rehabilitation, yet there is a significant shortage of skilled rehabilitation practitioners, particularly in low- and middle-income countries, with only 10 per 1 million population according to World Health Organization(WHO). The global demand for rehabilitation services, exacerbated by the COVID-19 pandemic, underscores the need for innovative solutions to improve accessibility and efficiency. Instead of increasing the number of physiotherapists, This research focuses on enhancing physiotherapist productivity by monitoring more patients simultaneously through home-based rehabilitation. This study investigates the integration of Human-Computer Interaction (HCI), computer vision, and sensor technologies to transform physical therapy. Key challenges include ensuring model generalizability, various data acquisition sensors, and overcoming barriers to real-world implementation. A comprehensive framework is proposed for home-based rehabilitation, utilizing HCI, computer vision, and sensor technologies to automate exercise assessment and classification. This framework aims to enable personalized rehabilitation programs and alleviate the strain on healthcare systems.

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