A Pilot Study for Developing Mobile App and Cloud Computing for Upper Extremities Motion Analysis

Résumé fourni par la source

Shoulder and elbow range of motion (ROM) impairment significantly affects an individual's quality of life, yet existing healthcare solutions for evaluating ROMs are often limited by cost, time, and accessibility constraints, particularly in smaller cities. In response, this paper presents a case study on a novel mobile and cloud computing application leveraging Machine Learning and Artificial Intelligence advancements to address these challenges. ROMs calculated by the mobile application are evaluated against a reference standard of motion capture (Qualisys 10-camera system) for specific upper extremity movements. Results demonstrate the proposed mobile and cloud application's accurate ROM measurement, with slight deviations at peak ROM compared to the Qualisys system. By providing a mobile, cost-effective solution, the proposed application aims to enhance diagnostic capabilities and address the critical need for automatic assessment of motions to support clinical and healthcare decision - making.

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