TOSHFA: A Mobile VR-Based System for Pose-Guided Exercise Rehabilitation for Low Back Pain

2026-01-24

AI Summary

This study addresses the challenges of insufficient professional supervision and low patient adherence in home-based low back pain rehabilitation by proposing a low-cost, mobile virtual reality (VR) rehabilitation system. The system leverages a standard laptop webcam combined with MediaPipe to estimate user skeletal keypoints in real time, transmitting this data via low-latency UDP to a Cardboard-style VR headset. To enhance engagement and adherence, it incorporates gamification elements such as point-based rewards and streak-based check-ins to guide therapeutic exercises. As the first home rehabilitation solution integrating lightweight mobile VR with real-time pose estimation, the system demonstrated strong user experience and technical feasibility in a pilot study with 20 participants, laying the groundwork for future multi-exercise clinical trials.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Low back pain (LBP) is a pervasive global health challenge, affecting approximately 80% of adults and frequently progressing into chronic or recurrent episodes. While exercise therapy is a primary clinical intervention, traditional at-home programs suffer from low adherence rates and the absence of professional supervision. This study introduces TOSHFA, an accessible mobile VR-based rehabilitation system that bridges this gap by combining computer vision with affordable hardware. The system utilizes a laptop webcam to perform real-time pose estimation via the MediaPipe framework, tracking 33 skeletal landmarks to provide immediate biofeedback. This data is streamed via low-latency UDP protocols to a smartphone mounted in a cardboard-style VR headset, where patients interact with a gamified 3D environment. A pilot study with 20 participants evaluated the system's performance and user engagement. Quantitative results yielded a mean System Usability Scale (SUS) score of 47.4, indicating marginal usability and a need for interface optimization. However, Game Experience Questionnaire (GEQ) data revealed high scores in positive affect and enjoyment, suggesting that the gamification elements--such as coin rewards and streak tracking--successfully maintained user motivation despite technical friction. These findings validate the feasibility of a smartphone-based tele-rehabilitation model and establish a technical foundation for future clinical trials involving multi-exercise protocols.

Problem

• low back pain
• exercise rehabilitation
• adherence
• remote supervision
• tele-rehabilitation

Innovation

• mobile VR
• pose estimation
• gamified rehabilitation
• tele-rehabilitation
• MediaPipe