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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22.	赵丽娜, 刘作军, 苟斌, 等. 基于隐马尔可夫模型的动力型下肢假肢步态预识别. 机器人, 2014, 36(3): 337-341.
23.	王永雄, 陈晗, 尹钟, 等. 基于惯导信息的人体动作和路况识别. 生物医学工程学杂志, 2018, 35(4): 621-630.
24.	史峰, 王小川, 郁磊, 等. MATLAB神经网络30个案例分析. 北京: 北京航空航天大学出版社, 2010.
25.	宣伯凯, 刘作军, 陈玲玲, 等. 膝上型假肢的运动意图识别与控制. 东南大学学报(自然科学版), 2017, 47(6): 1107-1116.

1. 侯增广, 赵新刚, 程龙, 等. 康复机器人与智能辅助系统的研究进展. 自动化学报, 2016, 42(12): 1765-1779.
2. 佀国宁, 黄琬婷, 李根生, 等. 下肢外骨骼机器人柔顺特性的研究进展. 生物医学工程学杂志, 2019, 36(1): 157-163.
3. 谢峥, 王明江, 黄武龙, 等. 基于实时步态分析的行走辅助外骨骼机器人系统. 生物医学工程学杂志, 2017, 34(2): 265-270.
4. 魏小东, 孟青云, 喻洪流, 等. 下肢外骨骼机器人研究进展. 中国康复医学杂志, 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. 陈建华, 奚如如, 王兴松, 等. 外骨骼机器人的非结构地面行走步态分类算法. 机器人, 2017, 39(4): 505-513.
8. 丁其川, 熊安斌, 赵新刚, 等. 基于表面肌电的运动意图识别方法研究及应用综述. 自动化学报, 2016, 42(1): 13-25.
9. 明东, 蒋晟龙, 王忠鹏, 等. 基于人机信息交互的助行外骨骼机器人技术进展. 自动化学报, 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. 张鋆豪, 何百岳, 杨旭升, 等. 基于可穿戴式惯性传感器的人体运动跟踪方法综述. 自动化学报, 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. 张向刚, 唐海, 付常君, 等. 一种基于隐马尔科夫模型的步态识别算法. 计算机科学, 2016, 43(7): 285-289.
18. 郝静涵, 杨鹏, 陈玲玲, 等. 一种基于表面肌电信号及三轴加速度信号的步态识别方法. 中国组织工程研究, 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. 赵丽娜, 刘作军, 苟斌, 等. 基于隐马尔可夫模型的动力型下肢假肢步态预识别. 机器人, 2014, 36(3): 337-341.
23. 王永雄, 陈晗, 尹钟, 等. 基于惯导信息的人体动作和路况识别. 生物医学工程学杂志, 2018, 35(4): 621-630.
24. 史峰, 王小川, 郁磊, 等. MATLAB神经网络30个案例分析. 北京: 北京航空航天大学出版社, 2010.
25. 宣伯凯, 刘作军, 陈玲玲, 等. 膝上型假肢的运动意图识别与控制. 东南大学学报(自然科学版), 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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