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Deep learning analysis based on multi-sensor fusion data for hemiplegia rehabilitation training system for stoke patients

Published online by Cambridge University Press:  26 July 2021

Peng Zhang  and
Junxia Zhang Open the ORCID record for Junxia Zhang [Opens in a new window]
Peng Zhang
Affiliation:
Tianjin University of Science and Technology, Tianjin 300222, China
Junxia Zhang*
Affiliation:
Tianjin Key Laboratory of Integrated Design and On-line Monitoring of Light Industry and Food Engineering Machinery and Equipment, Tianjin 300222, China
*
*Corresponding author. Email: zjx@tust.edu.cn
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Abstract

By recognizing the motion of the healthy side, the lower limb exoskeleton robot can provide therapy to the affected side of stroke patients. To improve the accuracy of motion intention recognition based on sensor data, the research based on deep learning was carried out. Eighty healthy subjects performed gait experiments under five different gait environments (flat ground, 10${}^\circ$ upslope and downslope, and upstairs and downstairs) by simulating stroke patients. To facilitate the training and classification of the neural network, this paper presents template processing schemes to adapt to different data formats. The novel algorithm model of a hybrid network model based on convolutional neural network (CNN) and Long–short-term memory (LSTM) model is constructed. To mitigate the data-sparse problem, a spatial–temporal-embedded LSTM model (SQLSTM) combining spatial–temporal influence with the LSTM model is proposed. The proposed CNN-SQLSTM model is evaluated on a real trajectory dataset, and the results demonstrate the effectiveness of the proposed model. The proposed method will be used to guide the control strategy design of robot system for active rehabilitation training.

Keywords

motion intention recognitiondeep learninglower limb exoskeleton robotmulti-sensor fusion dataCNN-SQLSTM model

Information

Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 780 - 797
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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