Electromyography Acquisition System Via Conductive Fabric For Wearable Skill Transfer Device Focused On Drum-Oriented Activities

This research seeks to establish a precedent for future work in motor skill transfer using a human-to-human interface system. We propose an integrated wearable device for electromyography signal acquisition, an AI-based data classification algorithm and a graphic interface for signal visu-alization. Our proposed design is based on intelligent wearable system composed of a shirt with Nylon-silver fabric electrodes that emulate a conventional textile. The textile electrodes are embedded in body locations optimized to detect muscle signals corresponding to drum playing activity. We demonstrate that the proposed textile electrodes are capable to monitor optimal EMG signal that is useful to train an AI algorithm to classify diverse types of drum strokes, positions and strength level.