Development of a Smartphone-Based Motion Evaluation App: Real-Time Multi-Joint Analysis For Enhanced Rehabilitation Monitoring

Nesma Mohamed Ezzat Ahmed （2025年3月博士前期課程修了）

This work suggests the design of a smartphone-based motion measurement application using MediaPipe, an open-source machine learning framework created by Google. MediaPipe allows real-time pose estimation, enabling the tracking of many joint angles throughout different rehabilitation activities using a standard smartphone camera. This creative technique seeks to give an easily available, user-friendly instrument for evaluating motor abilities in both clinical and home settings, therefore enhancing rehabilitation results for those with musculoskeletal diseases. Leveraging the general availability of smartphones, this study solves the major obstacles experienced by patients who might not have access to conventional rehabilitation centers.