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Hand tracking is a critical component of natural user interactions inextended reality (XR) environments, including extended reality musicalinstruments (XRMIs). However, self-occlusion remains a significant challengefor vision-based hand tracking systems, leading to inaccurate results anddegraded user experiences. In this paper, we propose a multimodal hand trackingsystem that combines vision-based hand tracking with surface electromyography(sEMG) data for finger joint angle estimation. We validate the effectiveness ofour system through a series of hand pose tasks designed to cover a wide rangeof gestures, including those prone to self-occlusion. By comparing theperformance of our multimodal system to a baseline vision-based trackingmethod, we demonstrate that our multimodal approach significantly improvestracking accuracy for several finger joints prone to self-occlusion. Thesefindings suggest that our system has the potential to enhance XR experiences byproviding more accurate and robust hand tracking, even in the presence ofself-occlusion.

Combining Vision and EMG-Based Hand Tracking for Extended Reality Musical Instruments