Training Smarter with OpenEarable: A Boxing Gesture Recognition Dashboard Integration
Year: 2024
Type: conference-paper
Abstract: Earables, wearable devices worn around the ear, offer new possibilities for sports applications requiring precise head movement analysis, such as boxing. However, boxing-specific gesture recognition using IMU sensors integrated into earables remains underexplored. This work addresses this gap by investigating the potential of the open-source OpenEarable platform for real-time recognition of defensive boxing manoeuvres, including slipping, rolling and pulling back. We propose an extension to OpenEarable, integrating a Python server that leverages machine learning and dynamic time warping for gesture recognition. Furthermore, the web dashboard is enhanced to enable server communication and implement a gesture mirroring feature, providing real-time visual feedback. Real-time testing achieved a high accuracy of 96%, with feedback delivered within one second. All the system components are made available in a GitHub repository.
Authors: Thomas Sepanosian, Ozlem Durmaz Incel
Institution: University of Twente
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G5235525701
OpenAlex ID: W4402721787