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An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection

Author

Listed:
  • Tao Zhen

    (College of Engineering, Beijing Forestry University, Beijing 100083, China)

  • Lei Yan

    (College of Engineering, Beijing Forestry University, Beijing 100083, China)

  • Jian-lei Kong

    (Artificial Intelligence Academy, Beijing Technology and Business University, Beijing 100048, China
    National Key Laboratory of Environmental Protection Food Chain Pollution Prevention, Beijing 100048, China)

Abstract

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation—while ignoring the inherent correlation in high-dimensional spaces—which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

Suggested Citation

  • Tao Zhen & Lei Yan & Jian-lei Kong, 2020. "An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection," IJERPH, MDPI, vol. 17(16), pages 1-17, August.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5633-:d:394623
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    References listed on IDEAS

    as
    1. Lei Yan & Tao Zhen & Jian-Lei Kong & Lian-Ming Wang & Xiao-Lei Zhou, 2020. "Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network," Complexity, Hindawi, vol. 2020, pages 1-14, January.
    2. Yu-ting Bai & Xue-bo Jin & Xiao-yi Wang & Xiao-kai Wang & Ji-ping Xu, 2020. "Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis," IJERPH, MDPI, vol. 17(1), pages 1-19, January.
    3. Xue-Bo Jin & Xing-Hong Yu & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    4. Xue-Bo Jin & Nian-Xiang Yang & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction," Mathematics, MDPI, vol. 8(2), pages 1-17, February.
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