Real-Time Exercise Feedback through a Convolutional Neural Network: A Machine Learning-Based Motion-Detecting Mobile Exercise Coaching Application
Jinyoung Park, Seok Young Chung, Jung Hyun Park
Abstract
PURPOSE: Mobile applications are widely used in the healthcare market. This study aimed to determine whether exercise using a machine learning-based motion-detecting mobile exercise coaching application (MDMECA) is superior to video streaming-based exercise for improving quality of life and decreasing lower back pain. MATERIALS AND METHODS: The same 14-day daily workout program consisting of five exercises was performed by 104 participants using the MDMECA and another 72 participants using video streaming. The Medical Outcomes Study Short Form 36-Item Health Survey (SF-36) and lower back pain scores were assess as pre- and post-workout measurements. Scores for the treatment-satisfaction subscale of the visual analog scale (TS-VAS), intention to use a disease-oriented exercise program, intention to recommend the program to others, and available expenses for a disease-oriented exercise program were determined after the workout. RESULTS: <0.01) in the MDMECA group. However, the available expenses for a disease-oriented program were not significantly different between the two groups. CONCLUSION: The MDMECA is more effective than video streaming-based exercise in increasing exercise adherence, improving QoL, and reducing lower back pain. MDMECAs could be promising tools of use to achieve better medical outcomes and higher treatment satisfaction.