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- Title
A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton.
- Authors
Zhang, Yuepeng; Cao, Guangzhong; Ling, Ziqin; Li, WenZhou; Cheng, Haoran; He, Binbin; Cao, Shengbin; Zhu, Aibin
- Abstract
Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.
- Subjects
ROBOTIC exoskeletons; GAIT in humans; LEG; KNEE; CONVOLUTIONAL neural networks
- Publication
Frontiers in Neurorobotics, 2021, Vol 2021, p1
- ISSN
1662-5218
- Publication type
Article
- DOI
10.3389/fnbot.2021.692539