A pace recognition method for exoskeleton wearers based on support vector machine-hidden Markov model_Journal of Biomedical Engineering

Authors：

HU Dong ,  LIU Zuojun , CHEN Lingling , WANG Qian

The School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, P. R. China;

Corresponding?author：

LIU Zuojun, Email: liuzuojun@hebut.edu.cn

Keywords：

Pace recognition; Lower extremity exoskeleton; Support vector machine; Hidden Markov model; Motion intent

DOI：

10.7507/1001-5515.202011009

Video：

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Abstract Full text Figures/Tables Video References Cited by

In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.

Citation： HU Dong, LIU Zuojun, CHEN Lingling, WANG Qian. A pace recognition method for exoskeleton wearers based on support vector machine-hidden Markov model. Journal of Biomedical Engineering, 2022, 39(1): 84-91. doi: 10.7507/1001-5515.202011009 Copy

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20.	Attal F, Amirat Y, Chilbani A, et al. Automatic recognition of gait phases using a multiple-regression hidden Markov model. IEEE-ASME T Mech, 2018, 23(4): 1597-1607.
21.	Ma H, Liao W H. Human gait modeling and analysis using a semi-Markov process with ground reaction forces. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(6): 597-607.
22.	趙麗娜, 劉作軍, 茍斌, 等. 基于隱馬爾可夫模型的動力型下肢假肢步態預識別. 機器人, 2014, 36(3): 337-341.
23.	王永雄, 陳晗, 尹鐘, 等. 基于慣導信息的人體動作和路況識別. 生物醫學工程學雜志, 2018, 35(4): 621-630.
24.	史峰, 王小川, 郁磊, 等. MATLAB神經網絡30個案例分析. 北京: 北京航空航天大學出版社, 2010.
25.	宣伯凱, 劉作軍, 陳玲玲, 等. 膝上型假肢的運動意圖識別與控制. 東南大學學報(自然科學版), 2017, 47(6): 1107-1116.

1. 侯增廣, 趙新剛, 程龍, 等. 康復機器人與智能輔助系統的研究進展. 自動化學報, 2016, 42(12): 1765-1779.
2. 佀國寧, 黃琬婷, 李根生, 等. 下肢外骨骼機器人柔順特性的研究進展. 生物醫學工程學雜志, 2019, 36(1): 157-163.
3. 謝崢, 王明江, 黃武龍, 等. 基于實時步態分析的行走輔助外骨骼機器人系統. 生物醫學工程學雜志, 2017, 34(2): 265-270.
4. 魏小東, 孟青云, 喻洪流, 等. 下肢外骨骼機器人研究進展. 中國康復醫學雜志, 2019, 34(4): 491-495.
5. Koenig A, Binder C, Zitzewitz J V, et al. Voluntary gait speed adaptation for robot-assisted treadmill training// 2009 IEEE International Conference on Rehabilitation Robotics. Kyoto: IEEE, 2009: 419-424.
6. Adiputra D, Abdul Rahman M A, Ubaidillah, et al. Control reference parameter for stance assistance using a passive controlled ankle foot orthosis a preliminary study. Appl Sci, 2019, 9(20): 4416.
7. 陳建華, 奚如如, 王興松, 等. 外骨骼機器人的非結構地面行走步態分類算法. 機器人, 2017, 39(4): 505-513.
8. 丁其川, 熊安斌, 趙新剛, 等. 基于表面肌電的運動意圖識別方法研究及應用綜述. 自動化學報, 2016, 42(1): 13-25.
9. 明東, 蔣晟龍, 王忠鵬, 等. 基于人機信息交互的助行外骨骼機器人技術進展. 自動化學報, 2017, 43(7): 1089-1100.
10. Choi T, Im C, Kim S, et al. Prediction method of walking speed at swing phase using soleus electromyogram signal at previous stance phase// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE, 2018: 2308-2311.
11. Jung C, Choi J, Park S, et al. A methodology to control walking speed of robotic gait rehabilitation system using feasibility-guaranteed trajectories// 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg: IEEE, 2015: 5617-5622.
12. Song Y, Shin S, Kim S, et al. Speed estimation from a tri-axial accelerometer using neural networks// 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon: IEEE, 2007: 3224-3227.
13. 張鋆豪, 何百岳, 楊旭升, 等. 基于可穿戴式慣性傳感器的人體運動跟蹤方法綜述. 自動化學報, 2019, 45(8): 1439-1454.
14. Zheng E, Wang Q. Noncontact capacitive sensing-based locomotion transition recognition for amputees with robotic transtibial prostheses. IEEE Trans Neural Syst Rehabil Eng, 2016, 25(2): 161-170.
15. Young A J, Simon A M, Hargrove L J. A training method for locomotion mode prediction using powered lower limb prostheses. IEEE Trans Neural Syst Rehabil Eng, 2014, 22(3): 671-677.
16. Preece S J, Goulermas J Y, Kenney L P J, et al. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng, 2009, 56(3): 871-879.
17. 張向剛, 唐海, 付常君, 等. 一種基于隱馬爾科夫模型的步態識別算法. 計算機科學, 2016, 43(7): 285-289.
18. 郝靜涵, 楊鵬, 陳玲玲, 等. 一種基于表面肌電信號及三軸加速度信號的步態識別方法. 中國組織工程研究, 2019, 23(32): 5164-5169.
19. Wang X, Feng S, Yan W Q. Human gait recognition based on self-adaptive hidden markov model. IEEE-ACM Trans Comput Biol Bioinform, 2021, 18(3): 963-972.
20. Attal F, Amirat Y, Chilbani A, et al. Automatic recognition of gait phases using a multiple-regression hidden Markov model. IEEE-ASME T Mech, 2018, 23(4): 1597-1607.
21. Ma H, Liao W H. Human gait modeling and analysis using a semi-Markov process with ground reaction forces. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(6): 597-607.
22. 趙麗娜, 劉作軍, 茍斌, 等. 基于隱馬爾可夫模型的動力型下肢假肢步態預識別. 機器人, 2014, 36(3): 337-341.
23. 王永雄, 陳晗, 尹鐘, 等. 基于慣導信息的人體動作和路況識別. 生物醫學工程學雜志, 2018, 35(4): 621-630.
24. 史峰, 王小川, 郁磊, 等. MATLAB神經網絡30個案例分析. 北京: 北京航空航天大學出版社, 2010.
25. 宣伯凱, 劉作軍, 陳玲玲, 等. 膝上型假肢的運動意圖識別與控制. 東南大學學報(自然科學版), 2017, 47(6): 1107-1116.

Journal of Biomedical Engineering

A pace recognition method for exoskeleton wearers based on support vector machine-hidden Markov model

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