Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Wearable optomyography enables continuous neuroprosthetic control

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月21日

查看全文
  • Roman Khalikov,
  • Gurgen Soghoyan,
  • Mikhail Sintsov,
  • Mikhail Lebedev

摘要

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.