The research proposed a deployable and low-computing module for weightlifting performance analysis. As the field of weightlifting analysis progresses, there are promising opportunities for future development. One such opportunity is deploying the system in the wild conditions and adapting the pipeline for mobile devices.
The current study aims to employ machine learning techniques to accurately classify the type and range of punches thrown by boxers, utilizing the data obtained from Inertial Measurement Unit (IMU) sensors.
The current study uses machine learning techniques to classify boxers' punch type and punch range using IMU sensor data. These classifications are important for improving the boxer's performance in aspects such as offense, defense, and overall control of the match.
In this work, we introduce a vision-based system that relies on economic video capture system (single fixed viewpoint). Specifically, we propose modified algorithms for automatic transition detection system and person re-identification systems that shows significant improvements over state-of-the-art techniques.
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