Jump Rope Exercise Assistance Program - Jin-Woong Kim; Jae-Woo Shin; Seoung-Ho Choi (IEEE Access)
Jump rope exercise requires a fast tempo and breathing, which often leads to the problem of users forgetting their jump count during the workout. To address this issue, we propose a jump rope exercise assistance program that recognizes the user’s jump rope motions and analyzes the impact of joint coordinates on these motions. The proposed solution extracts frame-by-frame joint coordinate data from jump rope performance videos. It then utilizes artificial intelligence models to recognize jump rope motions and measure the jump count through motion recognition. We employed five machine learning models and two deep learning models to validate the jump rope motion recognition and count measurement. We analyzed the joint coordinates significantly influencing each jump rope motion using SHAP. Furthermore, we used Odds Ratios to analyze the jump rope motion occurrence probability based on joint coordinate values. Through these methods, we confirmed that the proposed solution effectively performs jump rope motion recognition and joint coordinate impact analysis for jump rope motions.