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Deep Spatial-Temporal Model for rehabilitation gait: optimal trajectory generation for knee joint of lower-limb exoskeleton

Du-Xin Liu (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China)
Xinyu Wu (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong)
Wenbin Du (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China)
Can Wang (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China)
Chunjie Chen (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China)
Tiantian Xu (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 7 August 2017

Abstract

Purpose

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

Design/methodology/approach

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

Findings

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Originality/value

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

Keywords

Acknowledgements

The work described in this paper is partially supported by the National Basic Research Program (Project No. 2015CB351706) and Guangdong Province Commonweal Research and Capability Building Project (2015A010103011). The authors would like to thank all subjects who participate in experiments, and the members of SIAT exoskeleton team for gait acquisition and experiments, and also, thank Ahmed El-Azab for English corrections.

Citation

Liu, D.-X., Wu, X., Du, W., Wang, C., Chen, C. and Xu, T. (2017), "Deep Spatial-Temporal Model for rehabilitation gait: optimal trajectory generation for knee joint of lower-limb exoskeleton", Assembly Automation, Vol. 37 No. 3, pp. 369-378. https://doi.org/10.1108/AA-11-2016-155

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited