The objective of this study is to classify the types of boxing punches using machine learning algorithms that processed skin-strain time history measurements from a self-adhesive, elastic fabric, wearable sensor called Motion Tape. A human participant study was designed to capture movements during boxing training. Subjects were asked to perform multiple sets of punches during the entire test, which consisted of jabs and hooks with and without striking a heavy bag. The collected Motion Tape data was used to train and compare time series classification algorithms to identify the types of punches performed and associated conditions. The results demonstrated that Motion Tape, in combination with machine learning techniques, could effectively classify different punch types based on skin-strain measurements. These findings highlighted the potential of the system as an effective tool for human performance analysis in sports and biomechanics applications.
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