Recognition of Walking Stance Phase and Swing Phase Based on Moving Window_Journal of Biomedical Engineering

Authors：

 GENGXiaobo ¹ , YANGPeng ^1,2 , WANGXinran ^1,3 , GENGYanli ¹ , HANYu ¹

1. College of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China;
2. Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin 300130, China;
3. College of Control Science and Engineering, Shandong University, Jinan 250061, China;

Corresponding author：

GENGXiaobo, Email: gengxiaobo223@163.com

Keywords：

surface electromyography signal; weighted filtering window; height permitted window; stance phase; swing phase

DOI：

10.7507/1001-5515.20140051

Video：

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Abstract Full text Figures/Tables Video References Cited by

Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.

Citation： GENGXiaobo, YANGPeng, WANGXinran, GENGYanli, HANYu. Recognition of Walking Stance Phase and Swing Phase Based on Moving Window. Journal of Biomedical Engineering, 2014, 31(2): 273-278. doi: 10.7507/1001-5515.20140051 Copy

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1. 高云园,佘青山,孟明,等.基于多源信息融合的膝上假肢步态识别方法[J].仪器仪表学报,2010,31(12):2682-2688.
2. 耿艳利,杨鹏,刘作军,等.大腿截肢者步速识别系统设计[J].中国康复医学杂志,2012,27(7):651-653.
3. 龚思远,杨鹏,刘启栋,等.智能下肢假肢传感器的应用[J].中国组织工程研究与临床康复,2010,14(17):3109-3112.
4. 孟明,佘青山,罗志增.HMM在下肢表面肌电信号步态识别中的应用[J].华中科技大学学报:自然科学版,2011,39(z2):176-179.
5. 王子洪,吴宝明,银健,等.具有人体活动情景辨识的穿戴式心电检测仪的研究[J].生物医学工程学杂志,2012,29(5):941-947.
6. HUANG H,KUIKEN T A,LIPSCHUTZ R D.A strategy for identifying locomotion modes using surface electromyography[J].IEEE Trans Biomed Eng,2009,56(1):65-73.
7. 俞向明,张跃.实时心电信号监控中ST段改变检测方法[J].计算机工程,2009,35(18):252-253,256.
8. HUANG H,ZHANG F,HARGROVE L J,et al.Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion[J].IEEE Trans Biomed Eng,2011,58(10):2867-2875.
9. HOF A L,ELZINGA H,GRIMMIUS W,et al.Speed dependence of averaged EMG profiles in walking[J].Gait Posture,2002,16(1):78-86.
10. 杨鹏,周丽红,陈玲玲,等.基于峰-谷分段积分算法的行走步态周期识别[J].计算机工程,2011,37(23):168-170.

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