Xiang Qian, Wang Jiaxin, Liu Yong, Guo Shijie, Liu Lei
Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, China.
The Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin 300401, China.
Bioengineering (Basel). 2024 Mar 13;11(3):275. doi: 10.3390/bioengineering11030275.
The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.
外骨骼的步态识别包括在各种路况下的运动识别和步态阶段识别。步态阶段的识别是预测外骨骼辅助时间的前提。实时辅助时间的估计对于下肢外骨骼的安全和精确控制至关重要。为了解决预测外骨骼辅助时间的问题，本文提出了一种基于惯性测量单元的步态识别模型，该模型结合了支持向量机的实时运动状态识别和长短期记忆网络的相位识别。对30名受试者进行了识别验证实验，以确定步态识别模型的可靠性。结果表明，运动状态和步态阶段的准确率分别为99.98%和98.26%。基于所提出的支持向量机-长短期记忆步态模型，对外骨骼辅助时间进行了预测。对10名受试者进行了测试，结果表明，基于运动状态和步态阶段的辅助治疗可以显著改善步态运动，并使代谢成本平均降低超过10%。
