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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Dynamic gesture recognition based on multiple sensors fusion technology.

Wang Wenhui1, Chen Xiang, Wang Kongqiao

  • 1Institute of Biomedical Engineering at the University of Science and Technology of China, Hefei, China. wwh9712@mail.ustc.edu.cn

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
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This study fuses accelerometer, EMG, and webcam data for dynamic gesture recognition. Combining these sensors significantly improves accuracy for recognizing 20 hand gestures.

Area of Science:

  • Human-computer interaction
  • Sensor fusion
  • Biomedical engineering

Background:

  • Dynamic gesture recognition is crucial for intuitive human-computer interaction.
  • Single-sensor systems often face limitations in accuracy and robustness.
  • Integrating multiple data streams offers potential for enhanced performance.

Purpose of the Study:

  • To investigate the effectiveness of combining a three-axis accelerometer, surface electromyography (sEMG) sensors, and a webcam for dynamic gesture recognition.
  • To propose and evaluate a decision-level multiple sensor fusion method based on action elements.
  • To distinguish between 20 different dynamic hand gestures.

Main Methods:

  • Simultaneous data collection from accelerometer, sEMG, and webcam during gesture execution.

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  • Development of a decision-level sensor fusion algorithm.
  • Comparative analysis of single-sensor versus multi-sensor fusion performance.
  • Experiments conducted with three human subjects.

    • Main Results:

    • Multi-sensor fusion achieved high recognition accuracies, ranging from 87.5% to 91.8%.
    • Performance significantly surpassed that of individual sensor conditions.
    • The proposed fusion method effectively distinguished between 20 dynamic hand gestures.

    Conclusions:

    • Combining accelerometer, sEMG, and webcam data via decision-level fusion enhances dynamic gesture recognition accuracy.
    • Multi-sensor fusion is a promising approach for robust and continuous gesture recognition.
    • This technology is valuable for developing advanced multi-modal interaction systems.