CASIA OpenIR  > 复杂系统管理与控制国家重点实验室
Spiking-Neural-Network Based Fugl-Meyer Hand Gesture Recognition For Wearable Hand Rehabilitation Robot
Liu, Yang1,2; Cheng, Long1,2
2018-07
Conference Name2018 International Joint Conference on Neural Networks
Source PublicationIJCNN
Volume2018
Issue7
Pages1423-1428
Conference Date2018-7
Conference PlaceRio de Janeiro,Brazil
Author of SourceIEEE
Publication PlaceUSA
PublisherIEEE
Abstract

Hand rehabilitation robot can assist the patients in completing rehabilitation exercises. Usually these rehabilitation exercises are designed according to Fugl-Meyer Assessment(FMA). Surface electromyography(sEMG) signal is the most commonly used physiological signal to identify the patient's movement intention. However, recognizing the hand gesture based on the sEMG signal is still a challenging problem due to the low amplitude and non-stationary characteristics of the sEMG signal. In this paper, eight standard hand movements in FMA are selected for the active exercises by hand rehabilitation robots. A total of 15 volunteers' sEMG signals are collected in the course of the experiment. Four time domain features, integral EMG(IEGM), root mean square(RMS), zero crossings(ZC) and energy percentage(EP), are used to identify hand gestures. A feedforward spiking neural network receives the above time domain feature data, and combines the population coding with the Spikeprop learning algorithm to realize the accurate recognition of hand gestures. The experimental results show that: (1) the spiking neural network can achieve a satisfactory classification accuracy by using only 15 neurons; (2) the classification accuracy using all four features are highest with an accuracy of 96.5%; (3) under the same number of neurons, the classification accuracy of the spiking neural network is higher than that of the multilayer perceptron, radial basis function network and support vector machine. This demonstrates the fact that spiking neural networks can achieve a satisfactory classification accuracy with a smaller network size.

KeywordSpiking Neural Networks, Surface Electromyography, Fugl-meyer Assessment, Hand Gesture Recogniton, Spikeprop
MOST Discipline Catalogue工学::控制科学与工程
DOI10.1109/IJCNN.2018.8489141
URL查看原文
Indexed ByEI
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Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23558
Collection复杂系统管理与控制国家重点实验室
Corresponding AuthorCheng, Long
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liu, Yang,Cheng, Long. Spiking-Neural-Network Based Fugl-Meyer Hand Gesture Recognition For Wearable Hand Rehabilitation Robot[C]//IEEE. USA:IEEE,2018:1423-1428.
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