Vision-Based Human Movement Matching for Muay Thai Training Support System

Abstract

An algorithm is proposed to evaluate Muay Thai trainees based on body movement matching with the trainer, aiming to support Muay Thai training. Skeletons of the trainee in image sequences captured by a monocular camera are extracted by a pre-trained deep-learning model. They are then synchronized with those of the trainer by matching keyframes and subsequently other image frames between both sequences using dynamic time warping. In each matched frame, the skeleton lines (bones) are compared by measuring the angle deviation of each bone from the trainee to the corresponding bone of the trainer. The similarity score of each bone pair is computed based on a cosine similarity metric. Simultaneously, a predefined upper-value limit of the maximum expected difference in bone comparison is applied to enhance the difference in similarity scores between excellent and underperformed postures, making the evaluation results more distinctive. Each matched frame is then scored by weighted averaging of bone scores, allowing the evaluation to be adaptively focused on significant parts of the body according to movement types. The overall score of the sequence is an average over all frames. Experimental results demonstrate that the proposed scoring method is flexible for use with an off-the-shelf camera and adaptive to a marking scheme that often changes with training strategies and patterns. Prior matching of keyframes reduces the mismatching of similar but non-corresponding frames. The score is also normalized to penalize misoriented bones, resulting in even lower scores for trainees with less skill.