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

TL;DR

TOSHFA is a mobile VR system that uses computer vision for pose-guided exercise rehabilitation targeting low back pain, demonstrating promising user engagement and technical feasibility for remote therapy.

Contribution

This work introduces a novel mobile VR-based rehabilitation system combining real-time pose estimation with gamified feedback for low back pain patients.

Findings

High user engagement in gamified environment

Marginal usability score indicating need for interface improvements

Feasibility of smartphone-based tele-rehabilitation demonstrated

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.