Sci Rep. 2026 Mar 21. doi: 10.1038/s41598-025-32646-y. Online ahead of print.
ABSTRACT
Wearable devices are increasingly used to enable human-machine interfaces, such as typing or cursor control, through wristbands that translate surface electromyographic (sEMG) signals into computer commands. However, traditional sEMG techniques face several limitations, including challenges with sensor fixation, signal cross-talk, instability over time, and susceptibility to electrical and mechanical artifacts. In this study, we propose an alternative approach to capturing and interpreting muscle activity using optomyography (OMG). Our OMG system - a wristband with 50 data channels, facilitates various computer mouse-like controls. Decoding is achieved through an efficient, compact, fully connected neural network trained on data from a center-out task performed with hand gestures. Eight able-bodied participants and one individual with limb loss successfully mastered OMG-based controls in tasks such as acquiring targets across various screen positions and playing Tetris. Performance improvements with training were assessed using metrics such as deviations from a straight trajectory, temporal deviation from an optimal path, and dwell time near the target prior to successful selection. These results highlight the potential of next-generation wearable devices to exceed conventional approaches in performance, accuracy, stability, and versatility.
PMID:41865023 | DOI:10.1038/s41598-025-32646-y