Abstract
This study designs a deep neural network to detect gait phases, including heel-strike (HS), foot-flat (FF), heel-off (HO) and swing (SW). Proposed the concept of “gait image” to be the input and “phase image” to be the output of the model. The model (CFCT) adopts Convolution layers to extract gait-image’s feature, Fully-Connected layers to vary the feature non-linearly and Convolution-Transpose layers to upgrade feature’s dimension to output the phase-image. The CFCT model is capable to predict multi-dimensional gait phases in 1.5 s time sequence and adapt to various walking posture and walking frequency of different people, indicating the model’s robustness. The maximum accuracy of current moment’s gait phase prediction is 98.37%. Results of predicted phases are analyzed according to the time sequence in past 1 s, current moment and future 0.5 s, remaining high and stable accuracy. The maximum accuracy is 96.80% at the time step of future 0.35 s, verifying the effectiveness and stability of the CFCT model.
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Acknowledgments
Research supported by the Research Project of China Disabled Persons’ Federation-on assistive technology (Grant No. 2021CDPFAT-27), the National Natural Science Foundation of China (Grant No. 51605339), and the Key Research and Development Program of Hubei Province (Grant NO. 2020BAB133).
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Liu, S., Zhou, Z., Lu, L., Xiao, X., Guo, Z. (2022). Gait Phase Detection Based on Time Sequence Adapting to Various Walking Posture and Frequency. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_5
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DOI: https://doi.org/10.1007/978-3-031-13835-5_5
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