Oct-HD: A Wearable Distributed Wireless HD-sEMG Synchronous Acquisition System for Long-Term Monitoring

• Published in: IEEE internet of things journal Vol. 12; no. 15; pp. 32245 - 32258
• Main Authors: Tan, Xin, Zhang, Yujie, Lin, Zhanhui, Zhu, Zhuozhuang, Chen, Wei, Xu, Ke, Dai, Chenyun
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.2025
• Subjects: Accuracy; Base station; Base stations; Biomedical monitoring; Electromyography; gesture recognition; high-density surface EMG (HD-sEMG); network-free synchronization; Real-time systems; Software; Synchronization; Universal Serial Bus; wearable EMG module; Wireless communication; Wireless sensor networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3577047

High-density surface EMG (HD-sEMG) is gaining attention because of its noninvasive nature and high spatial resolution. However, wearable HD-sEMG measurements with over 128 channels face challenges in system integration and reliable network-free interdevice synchronization. This article presents a wireless distributed wearable HD-sEMG acquisition system named Oct-HD, which supports up to eight acquisition modules (512 channels) with microsecond-level synchronization without requiring a network. The full-channel impedance detection ensures reliable electrode-skin contact and signal acquisition. The system also includes a self-locking base station that stores, charges, and configures the modules. Both simulated and real-world validation demonstrate that the system maintains a long-term intermodule offline error within 3 ms, even under vibrations and extreme temperatures, demonstrating reliability for network-free outdoor and open-space monitoring. To support high-level signal interpretation, we further developed an integrated analysis software suite alongside the hardware. This toolkit enables motor unit decomposition, feature extraction, root mean square (RMS) map and power spectral density (PSD) analysis. Comparative experiments with a commercial system (Sessantaquattro) involving ten hand postures and 17 subjects were conducted, as hand gesture recognition is one of the most common applications in the field of electromyography. Results showed a significant performance improvement (<inline-formula> <tex-math notation="LaTeX">{p} \lt 10^{-5} </tex-math></inline-formula>) of Oct-HD over the state-of-the-art system in signal quality and anti-interference capacity. Gesture classification results across four mainstream models demonstrated general accuracy improvements with Oct-HD over the commercial system in both dynamic and maintenance tasks. This highlights the effectiveness of Oct-HD in enhancing applications in prosthetic control and human-computer interaction. The Oct-HD system offers a notable advancement in wireless HD-sEMG acquisition, offering superior channel capacity, synchronization precision, signal quality, and anti-interference capacity compared to existing systems. The network-free microsecond-level synchronization and enhanced signal performance provide greater monitoring flexibility across various muscle groups, paving the way for broader applications in human-machine interaction.