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Advancing biomedical engineering through a multi-modal sensor fusion system for enhanced physical training

  • Received: 03 August 2023 Revised: 14 September 2023 Accepted: 24 September 2023 Published: 17 October 2023
  • In this paper, we introduce a multi-modal sensor fusion system designed for biomedical engineering, specifically geared toward optimizing physical training by collecting detailed body movement data. This system employs inertial measurement units, flex sensors, electromyography sensors, and Microsoft's Kinect V2 to generate an in-depth analysis of an individual's physical performance. We incorporate a gated recurrent unit- recurrent neural network algorithm to achieve highly accurate body and hand motion estimation, thus surpassing the performance of traditional machine learning algorithms in terms of accuracy, precision, recall, and F1 score. The system's integration with the PICO 4 VR environment creates a rich, interactive experience for physical training. Unlike conventional motion capture systems, our sensor fusion system is not limited to a fixed workspace, allowing users to engage in exercise within a flexible, free-form environment.

    Citation: Yi Deng, Zhiguo Wang, Xiaohui Li, Yu Lei, Owen Omalley. Advancing biomedical engineering through a multi-modal sensor fusion system for enhanced physical training[J]. AIMS Bioengineering, 2023, 10(4): 364-383. doi: 10.3934/bioeng.2023022

    Related Papers:

  • In this paper, we introduce a multi-modal sensor fusion system designed for biomedical engineering, specifically geared toward optimizing physical training by collecting detailed body movement data. This system employs inertial measurement units, flex sensors, electromyography sensors, and Microsoft's Kinect V2 to generate an in-depth analysis of an individual's physical performance. We incorporate a gated recurrent unit- recurrent neural network algorithm to achieve highly accurate body and hand motion estimation, thus surpassing the performance of traditional machine learning algorithms in terms of accuracy, precision, recall, and F1 score. The system's integration with the PICO 4 VR environment creates a rich, interactive experience for physical training. Unlike conventional motion capture systems, our sensor fusion system is not limited to a fixed workspace, allowing users to engage in exercise within a flexible, free-form environment.



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    Acknowledgments



    This study was supported by the 2021 Scientific Research Project of Hunan Provincial Department of Education under Grant No. 21C0845.

    Conflict of interest



    The authors declare that there is no conflict of interest.

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