(untitled) (International Conference on Computers in Education, 2023)
Squat training is highly effective for improving lower limb muscle function. However, when this type of training is performed alone without a personal trainer or physical therapist, it can lead to inefficiencies and injuries among trainees. Thus, we aim to develop a system that provides visual and real-time feedback on the correct posture of a lone squat trainee using pose estimation. In this study, we propose a function that demonstrates the correct posture for each squatting discipline in the form of a line-segment posture representation and warns the trainee when he/she assumes an incorrect posture. Squatting motion in the sagittal plane was captured using a camera connected to a personal computer, and the coordinates of the acromion, hip, knee, and ankle joints were detected using MoveNet. The joint angles were calculated from the detected coordinates and the correct posture was determined according to the individual’s body shape. The color of the formed line-segment posture representation changed to indicate the difference between the trainees’ actual and correct postures. In future research, we plan to assess the displacement of key points detected by MoveNet using optical technologies to confirm the reliability of this system’s pose estimation. Afterwards, we will evaluate the effectiveness of this system in determining whether a trainee can move appropriately.