A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton
| Autor: | Wen-Zhou Li, Bin-Bin He, Guang-Zhong Cao, Hao-Ran Cheng, Zi-Qin Ling, Yue-Peng Zhang, Sheng-Bin Cao, Ai-Bin Zhu |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
medicine.medical_treatment Biomedical Engineering real-time Neurosciences. Biological psychiatry. Neuropsychiatry lower limb rehabilitation exoskeleton Electromyography gait phase classification Convolutional neural network Lower limb Gait phase sEMG Gait (human) Artificial Intelligence multi-information fusion medicine Original Research convolutional neural network (CNN) Rehabilitation medicine.diagnostic_test business.industry Pattern recognition Exoskeleton Information fusion ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business RC321-571 Neuroscience |
| Zdroj: | Frontiers in Neurorobotics Frontiers in Neurorobotics, Vol 15 (2021) |
| ISSN: | 1662-5218 |
| DOI: | 10.3389/fnbot.2021.692539 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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