Boxing Gesture Recognition in Real-Time using Earable IMUs

This paper investigates the potential of earables for real-time boxing gesture recognition. While prior research explores earables in sports, there is a gap in applying them to boxing, particularly for defensive manoeuvre recognition. We address this gap by exploring the capability of real-time Inertial Measurement Unit (IMU)-based boxing head gesture recognition using the open-source OpenEarable framework. We employ classical machine learning and dynamic time-warping (DTW) approaches. A dataset across left/right slips, rolls, and pullbacks is collected from a hobbyist boxer. Our results suggest that DTW combined with gesture templates derived from barycenter averaging achieves high gesture recognition accuracy. The implemented algorithm achieves a testing accuracy of 99% on the collected dataset. This performance is further validated in a real-world scenario, where the algorithm maintains an overall accuracy of 96%. Additionally, the system demonstrates robustness to variations in gesture execution speed and intensity.