CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Towards Robot-Assisted Post-Stroke Hand Rehabilitation:Fugl-Meyer Gesture Recognition Using sEMG
Chen M(陈妙)1,2; Cheng L(程龙)1,2; Huang FB(黄富表)3; Yan Y(闫研)1; Hou ZG(侯增广)1
Conference NameThe 7th Aunual IEEE Iternational Conference on Cyber Technology in Automation, Control and Intelligent Systems
Conference Date2017.7.31-2017.8.4
Conference PlaceHawaii
AbstractRobot-assisted rehabilitation training requires to identify the patient’s motion intention effectively. These motions are usually originated from rehabilitation actions included in the Fugl-Meyer assessment scale. Surface electromyography (sEMG) is the most commonly used physiological signal for identifying the motion intention of patients. The use of sEMG to classify different gesture patterns is one key technology for the human-machine interaction. Therefore, this paper investigates a Fugl-Meyer hand gesture recognition method towards robot-assisted hand rehabilitation. The experiment data set including eight hand gesture information is collected from six volunteers. Six single features (Difference Absolute Mean Value (DAMV), Integral of Absolute Value (IAV), Variance (VAR), Autoregressive Coefficients (AR), maximum value of Discrete Wave Transformation (DWTmax) and standard deviation of DiscreteWavelet Transform (DWTstd)) are used to recognize the gesture. The experimental results demonstrate that: (1) a segment length of 250 ms contains enough information to estimate the hand gestures and leaves sufficient time to do feature extraction and gesture recognition; (2) by comparing the performance of different single features, DWTstd wins the highest accuracy (i.e., 96%); (3) the combination of single features into a multi-feature can effectively improve the recognition accuracy, where the best performance is achieved by
multi-feature combining DAMV, IAV and AR under BP neural network classifier (the average accuracy is 97.71%); (4) as to different classifiers, BP neural network has a better performance than Support Vector Machine (SVM) and Extreme Learning Machine (ELM).
KeywordSurface Electromyography Gesture Recognition Fugl-meyer Assessment Scale Hand Rehabilitation Back Propagation Neural Network
Document Type会议论文
Recommended Citation
GB/T 7714
Chen M,Cheng L,Huang FB,et al. Towards Robot-Assisted Post-Stroke Hand Rehabilitation:Fugl-Meyer Gesture Recognition Using sEMG[C],2017.
Files in This Item: Download All
File Name/Size DocType Version Access License
cyber17-281.pdf(1213KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen M(陈妙)]'s Articles
[Cheng L(程龙)]'s Articles
[Huang FB(黄富表)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen M(陈妙)]'s Articles
[Cheng L(程龙)]'s Articles
[Huang FB(黄富表)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen M(陈妙)]'s Articles
[Cheng L(程龙)]'s Articles
[Huang FB(黄富表)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: cyber17-281.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.