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论文:2022,Vol:40,Issue(4):764-770 |
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引用本文： |
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孔德智, 王文东, 郭栋, 史仪凯. 基于sEMG的上肢关节角度预测方法研究[J]. 西北工业大学学报 |
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KONG Dezhi, WANG Wendong, GUO Dong, SHI Yikai. Study on upper limb joint angle prediction method based on sEMG[J]. Northwestern polytechnical university |
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| 基于sEMG的上肢关节角度预测方法研究 |
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| 孔德智, 王文东, 郭栋, 史仪凯 |
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| 西北工业大学 机电学院, 陕西 西安 710072 |
| 摘要: |
| 针对康复训练过程人机交互性差、人机耦合不足的问题,提出上肢关节角度预测模型并完成实验验证。基于表面肌电信号(sEMG)获得可良好表征上肢运动意图的混合向量;完成信号的预处理、特征优化并提取时域特征值;针对当前运动控制领域模型预测精度不理想、预测速度较慢的问题,采用最小二乘支持向量机(LSSVM)方法实现上肢关节角度预测。实验结果证明提出的预测模型可根据表面肌电信号与姿态信息良好地预测人体上肢关节运动轨迹,有效减少预测时滞与误差,在提升人机耦合性方面具有一定的优越性。 |
| 关键词:
上肢外骨骼
表面肌电信号
最小二乘支持向量机
连续运动估计
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| Study on upper limb joint angle prediction method based on sEMG |
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| KONG Dezhi, WANG Wendong, GUO Dong, SHI Yikai |
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| School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| Aiming at the problems of insufficient human-computer interaction and human-machine coupling in the rehabilitation training process, a prediction model of upper limb joint angle is proposed and verified by experiments. Firstly, a mixture vector that can well represent the motion intention of the upper limbs is obtained based on sEMG; secondly, the signal preprocessing, feature optimization and extraction of temporal eigenvalues are completed; finally, for the problems of unsatisfactory prediction accuracy and slow prediction speed of the current models in the field of motion control, the least square method (LSM) is adopted. The upper limb joint angle prediction is realized by multiplying the support vector machine (LSSVM) first. The experimental results show that the prediction model proposed in this paper can well predict the motion trajectory of the upper limb joints of the human body according to the sEMG and attitude information, effectively reduce the prediction time delay and error, and has certain advantages. |
| Key words:
upper limb exoskeleton
rehabilitation training
sEMG
LSSVM
continuous motion estimation
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| 收稿日期: 2021-10-12
修回日期:
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| DOI: 10.1051/jnwpu/20224040764 |
| 基金项目: 国家自然科学基金(51605385)、陕西省自然科学基础研究计划(2020JM-131)和广东省基础与应用基础研究基金(2019A1515111176)资助 |
| 通讯作者: 王文东(1984-),西北工业大学副教授、博士,主要从事康复外骨骼机器人、人机交互系统设计、柔顺控制方法研究。e-mail:wdwang@nwpu.edu.cn
Email：wdwang@nwpu.edu.cn |
| 作者简介: 孔德智(1992-),西北工业大学博士研究生,主要从事智能控制系统与关键技术、仿生机械与机器人技术研究。
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参考文献: |
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[1] 刘振宇, 伍晓强. 外骨骼式康复机器人的发展现状[J]. 高科技与产业化,2018(5):106-111 LIU Zhenyu, WU Xiaoqiang. The development status of exoskeleton rehabilitation robots[J]. High Technology and Industrialization, 2018(5): 106-111 (in Chinese)
[2] RAHMANI M, RAHMAN M H, GHOMMAM J. A 7-DoF upper limb exoskeleton robot control using a new robust hybrid controller[J]. International Journal of Control Automation and Systems, 2019, 17: 1-9
[3] 王文东, 肖孟涵, 孔德智, 等. 基于人机耦合模型的上肢康复外骨骼闭环PD迭代控制方法[J]. 机械工程学报,2021,57(21):11-21 WANG Wendong, XIAO Menghan, KONG Dezhi, et al. Closed-loop PD iterative control method of upper limb rehabilitation exoskeleton based on human-machine coupling model[J]. Chinese Journal of Mechanical Engineering, 2021,57(21):11-21 (in Chinese)
[4] 张蔚然, 鲁守银, 吴林彦, 等. 基于模糊补偿的主从式上肢外骨骼康复机器人训练控制方法[J]. 机器人,2019,41(1): 104-111 ZHANG Weiran, LU Shouyin, WU Linyan, et al. A master-slave upper limb exoskeleton rehabilitation robot training control method based on fuzzy compensation[J]. Robots, 2019,41(1): 104-111 (in Chinese)
[5] ZIA M, WARIS A, GILANI S O, et al. Multiday EMG-based classification of hand motions with deep learning techniques[J]. Sensors(Basel), 2018, 18(8): 2497
[6] MA Chenfei ,LIN Chuang, SAMUEL Oluwarotimi Williams, et al. Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach[J]. Biomedical Signal Processing and Control, 2021, 61: 102024
[7] AUNG Y M, AI-JUMAILY A. sEMG based ANN for shoulder angle prediction[J]. Procedia Engineering, 2012, 41: 1009-1015
[8] WANG S, GAO Y, ZHAO J, et al. Prediction of sEMG-based tremor joint angle using the RBF neural network[C]//2012 IEEE International Conference on Mechatronics and Automation, 2012: 2103-2108
[9] PAN L, ZHANG D, LIU J, et al. Continuous estimation of finger joint angles under different static wrist motions from surface EMG signals[J]. Biomedical Signal Processing and Control, 2014, 14: 265-271
[10] WAHYUNGGORO O, NUGROHO H A. Effect of window length on performance of the elbow-joint angle prediction based on electromyography[J]. Journal of Physics: Conference Series, 2017, 853(1): 12-14
[11] CHU J U, MOON I, MUN M S. A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand[J]. IEEE Trans on Biomedical Engineering, 2016, 53(11): 2232-2239
[12] 李自由, 赵新刚, 张弼, 等. 基于表面肌电的意图识别方法在非理想条件下的研究进展[J]. 自动化学报,2021,47(5): 955-969 LI Ziyou, ZHAO Xingang, ZHANG Bi, et al. Research progress of intent recognition method based on surface electromyography under non-ideal conditions[J]. Chinese Journal of Automation, 2021, 47(5): 955-969 (in Chinese)
[13] PHINYOMARK A, PHUKPATTARANONT P, LIMSAKUL C. Feature reduction and selection for EMG signal classification[J]. Expert Systems with Applications, 2012, 39(8): 7420-7431 |
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