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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Design and Testing of a Portable Wireless Multi-Node sEMG System for Synchronous Muscle Signal Acquisition and

Xiaoying Zhu1,2, Chaoxin Li1,2, Xiaoman Liu3

  • 1Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, China.

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Summary

This study introduces a portable multi-node surface electromyography (sEMG) system to overcome channel limitations. The system achieves 99.4% accuracy in recognizing grasping motions using the Gradient Boosting Decision Tree algorithm.

Keywords:
gesture recognitionmulti-channelsurface electromyography signalwireless transmission

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Human-Computer Interaction

Background:

  • Surface electromyography (sEMG) is crucial for muscle assessment and human-machine interaction.
  • Commercial sEMG devices often have limited channels, hindering multi-site signal acquisition.
  • There is a need for scalable and portable sEMG systems.

Purpose of the Study:

  • To design and implement a portable multi-node sEMG acquisition system.
  • To overcome the channel limitations of existing commercial sEMG devices.
  • To enable advanced applications like gesture recognition.

Main Methods:

  • Developed a multi-node sEMG system using STM32L442KCU6 microcontroller and onboard ADC.
  • Implemented analog filtering and data transmission via ESP8266 WiFi module using TCP protocol.
  • Configured Bluetooth broadcasting for scalability up to 40 sEMG nodes.
  • Applied a gesture recognition algorithm (Gradient Boosting Decision Tree) for motion identification.

Main Results:

  • The system successfully supports up to 40 sEMG detection nodes.
  • The Gradient Boosting Decision Tree algorithm achieved 99.4% recognition accuracy for grasping motions with two channels.
  • Demonstrated effective detection of grasping motions with varying channel configurations.

Conclusions:

  • The designed portable multi-node sEMG system effectively addresses channel limitations.
  • The system provides a scalable solution for comprehensive muscle function assessment.
  • High accuracy in gesture recognition highlights the system's potential in rehabilitation and human-machine interaction.