A Mobile-Phone Pose Estimation for Gym-Exercise Form Correction

People learn to perform exercises with good pose (or form) via research or instruction from an experienced individual such as a personal trainer, but 33.3% of injuries still occur due to incorrect form. It is known that the presence of a personal trainer causes a significant reduction in the rate that injuries occur. There are many possible reasons for this such as cost, scheduling limitations and desire to train alone. However, given that 91% of UK adults use a smartphone, a mobile APP could take on the role of a personal trainer. This paper presents a solution using machine learning and a novel proposed method of form anomaly detection to offer form corrections from live exercise video while only using the capabilities of a mobile device. Overall, the work in this paper is capable detecting incorrect exercise pose and offer valid corrections based on the detected anomalies. Experiments have been conducted on live video to judge the system performance in real-time. Keywords: Pose Estimation, Deep Learning, Mobile Application, Electronic Gym Instructor.