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

Tao Zhen, Lei Yan and Jian-lei Kong
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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

IJERPH, 2020, vol. 17, issue 16, 1-17

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%.

Keywords: gait-phase-recognition; FMS-Net; spatiotemporal networks; IMU signals; skip-connection structure (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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