Threshold-Free Phase Segmentation and Zero Velocity Detection for Gait Analysis Using Foot-Mounted Inertial Sensors
Gait analysis is a prosperous tool for the clinical evaluation and diagnosis. In this article, a portable gait analysis system based on foot-mounted inertial sensors is established. A threshold-free method using a long short-term memory recurrent neural network is constructed to segment four typical...
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| Published in | IEEE transactions on human-machine systems Vol. 53; no. 1; pp. 1 - 11 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2168-2291 2168-2305 |
| DOI | 10.1109/THMS.2022.3228515 |
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| Abstract | Gait analysis is a prosperous tool for the clinical evaluation and diagnosis. In this article, a portable gait analysis system based on foot-mounted inertial sensors is established. A threshold-free method using a long short-term memory recurrent neural network is constructed to segment four typical gait phases in a gait sequence for the temporal parameters analysis. Segmentation accuracy reaches over 95% across recruited subjects with distinct gait patterns, which is significantly superior when compared with traditional machine learning methods. The zero-velocity indicator is generated successively according to the segmented sequence to accomplish zero velocity update for the spatial parameter calculation. The accuracy of the proposed system is also validated through the OptiTrack in the lab. The comparison result of the stride length shows that the error between the two systems is less than 2%, which demonstrates that our system can satisfy the demand in the clinical. |
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| AbstractList | Gait analysis is a prosperous tool for the clinical evaluation and diagnosis. In this article, a portable gait analysis system based on foot-mounted inertial sensors is established. A threshold-free method using a long short-term memory recurrent neural network is constructed to segment four typical gait phases in a gait sequence for the temporal parameters analysis. Segmentation accuracy reaches over 95% across recruited subjects with distinct gait patterns, which is significantly superior when compared with traditional machine learning methods. The zero-velocity indicator is generated successively according to the segmented sequence to accomplish zero velocity update for the spatial parameter calculation. The accuracy of the proposed system is also validated through the OptiTrack in the lab. The comparison result of the stride length shows that the error between the two systems is less than 2%, which demonstrates that our system can satisfy the demand in the clinical. |
| Author | Shi, Xin Wang, Zhelong Liu, Ruichen Zhao, Hongyu Lin, Fang Qiu, Sen Tang, Kai |
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| SubjectTerms | Estimation Gait Gait analysis Inertial sensing devices Inertial sensors Labeling long short-term memory(LSTM) Machine learning Man-machine systems Manuals Micromechanical devices Parameters phase segmentation Quaternions Recurrent neural networks Segmentation zero velocity updates (ZUPT) |
| Title | Threshold-Free Phase Segmentation and Zero Velocity Detection for Gait Analysis Using Foot-Mounted Inertial Sensors |
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