采用表面肌电信号的手指关节角度精确感知方法

王海; 陶庆; 苏娜; 张小栋; 张天乐

doi:10.7652/xjtuxb202208017

您当前的位置：

首页 >

文章列表页 >

采用表面肌电信号的手指关节角度精确感知方法

—————————————————————— | 更新时间：2025-07-09

- 采用表面肌电信号的手指关节角度精确感知方法
- Method for Accurate Sensing of Finger Joint Angles Using Surface Electromyography Signals
- 西安交通大学学报 2022年56卷第8期页码：160-167
- 作者机构：
  
  1. 新疆大学机械工程学院,乌鲁木齐,830047
  2. 新疆医科大学第一附属医院,乌鲁木齐,830054
  3. 西安交通大学机械工程学院,西安,710049
- 作者简介：
  
  : 2022-03-07。作者简介: 王海(1996—),男,硕士生
  陶庆(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(51865056)
- 基金信息：
  
  : 2022-03-07。作者简介: 王海(1996—),男,硕士生;陶庆(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(51865056);自治区区域协同创新专项(科技援疆计划)资助项目(2020E0259)
- DOI：10.7652/xjtuxb202208017
  中图分类号： TP412
- 网络首发：2022-08-10，
  
  纸质出版：2022
- 稿件说明：
移动端阅览
王海, 陶庆, 苏娜, 等. 采用表面肌电信号的手指关节角度精确感知方法[J]. 西安交通大学学报, 2022,56(8):160-167.

WANG Hai, TAO Qing, SU Na, et al. Method for Accurate Sensing of Finger Joint Angles Using Surface Electromyography Signals[J]. 2022, 56(8): 160-167.
王海, 陶庆, 苏娜, 等. 采用表面肌电信号的手指关节角度精确感知方法[J]. 西安交通大学学报, 2022,56(8):160-167. DOI： 10.7652/xjtuxb202208017.

WANG Hai, TAO Qing, SU Na, et al. Method for Accurate Sensing of Finger Joint Angles Using Surface Electromyography Signals[J]. 2022, 56(8): 160-167. DOI： 10.7652/xjtuxb202208017.

摘要

针对目前采用肌电信号的手指关节角度连续解码误差较大

导致肌电假肢手运动效果较差的情况

提出了一种应用表面肌电信号、深度回归森林模型和人工神经网络相结合的手指关节角度连续精确感知方法。首先

应用基于滑动时间窗的特征提取器从前臂8个通道的肌电信号中各提取7种肌电信号特征(肌电信号的平均绝对值、积分肌电值、均方根、波形长度、对数特征、过零点数、斜率符号变化数)

输入深度森林回归模型得到具有较大波动的掌指关节估计角度; 然后

采用人工神经网络对这些掌指关节估计角度进行优化

以创建一种深度森林回归模型与人工神经网络相结合的综合回归模型; 最后

利用该综合回归模型对采集到的表面肌电信号进行连续精确解码

得到肌电假手掌指关节角度控制量

其余手指关节角度可通过比例控制得到。采用所提方法进行实验验证

结果表明:所提方法的平均轨迹跟踪精度比传统高斯过程方法提高了42%

达到82.12%

证明所提方法对基于肌电信号的手指关节角度估计具有非常优良的效果。

Abstract

To tackle the problem of poor movement of EMG-controlled prosthetic hand resulting from large continuous decoding errors of finger joint angles

this paper proposes a continuous and accurate sensing method that combines the deep regression forest model and artificial neural network based on EMG signals. Firstly

the feature extractor based on sliding time window is used to extract 7 kinds of EMG features(mean absolute value

integral EMG value

root mean square

waveform length

logarithmic feature

zero crossing points and slope symbol change)from the EMG signals of 8 channels of the forearm. These features are then input into the deep forest regression model to obtain the estimated angles of metacarpophalangeal joints with large fluctuation; then

the artificial neural network is used to optimize these estimated angles of metacarpophalangeal joints

so as to create a comprehensive regression model combining the deep forest regression model and artificial neural network; finally

the comprehensive regression model is used to continuously and accurately decode the collected surface EMG signals to obtain the control quantities of finger joint angles of the prosthetic hand

and other finger joint angles were obtained through proportional control. The experimental results show that the average trajectory tracking accuracy of the proposed method is 42% higher than that of the traditional Gaussian process method

reaching 82.12%. This proves that the proposed method can achieve excellent effects on finger joint angle estimation based on EMG signals.

关键词

Keywords

references

BEASLEY R W. General considerations in managing upper limb amputations [J]. Orthopedic Clinics of North America, 1981, 12(4): 743-749.

REITER R. Eine neu elecktrokunstand [J]. Grenzgebiete der Medicin, 1948, 1(4): 133-135.

胡旭晖, 宋爱国, 李会军. 基于表面肌电图像的灵巧假手控制系统 [J]. 控制理论与应用, 2018, 35(12): 1707-1714.

HU Xuhui, SONG Aiguo, LI Huijun. Dexterous robot hand control system based on surface electromyography image [J]. Control Theory Applications, 2018, 35(12): 1707-1714.

赵诗琪, 吴旭洲, 张旭, 等. 利用表面肌电进行手势自动识别 [J]. 西安交通大学学报, 2020, 54(9): 149-156.

ZHAO Shiqi, WU Xuzhou, ZHANG Xu, et al. Automatic gesture recognition with surface electromyography signal [J]. Journal of Xi'an Jiaotong University, 2020, 54(9): 149-156.

YAMANOI Y, OGIRI Y, KATO R. EMG-based posture classification using a convolutional neural network for a myoelectric hand [J]. Biomedical Signal Processing and Control, 2020, 55: 101574.

席旭刚, 杨晨, 罗志增, 等. 基于肌电复杂度和支持向量机的比例控制假手 [J]. 华中科技大学学报(自然科学版), 2017, 45(10): 59-64.

XI Xugang, YANG Chen, LUO Zhizeng, et al. Proportional control prosthetic hand based on electromyography complexity and support vector machine [J]. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2017, 45(10): 59-64.

隋修武, 刘乃嘉, 乔明敏, 等. 基于PSO-SVM的仿生肌电假手反馈控制系统设计 [J]. 传感技术学报, 2019, 32(12): 1784-1789.

SUI Xiuwu, LIU Naijia, QIAO Mingmin, et al. Design of prosthesis feedback control system based on PSO-SVM [J]. Chinese Journal of Sensors and Actuators, 2019, 32(12): 1784-1789.

CÔTÉ-ALLARD U, FALL C L, DROUIN A, et al. Deep learning for electromyographic hand gesture signal classification using transfer learning [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(4): 760-771.

HU Yu, WONG Y, WEI Wentao, et al. A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition [J]. PLoS One, 2018, 13(10): e0206049.

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 [J]. 自动化学报, 2016, 42(1): 13-25.

DING Qichuan, XIONG Anbin, ZHAO Xingang, et al. A Review on researches and applications of sEMG-based motion intent recognition methods [J]. Acta Automatica Sinica, 2016, 42(1): 13-25.

ZHANG Qin, PI Te, LIU Runfeng, et al. Simultaneous and proportional estimation of multijoint kinematics from EMG signals for myocontrol of robotic hands [J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(4): 1953-1960.

CHEN Yan, YU Song, MA Ke, et al. A continuous estimation model of upper limb joint angles by using surface electromyography and deep learning method [J]. IEEE Access, 2019, 7: 174940-174950.

CHEN Chen, MA Shihan, SHENG Xinjun, et al. Adaptive real-time identification of motor unit discharges from non-stationary high-density surface electromyographic signals [J]. IEEE Transactions on Biomedical Engineering, 2020, 67(12): 3501-3509.

FARINA D, VUJAKLIJA I, SARTORI M, et al. Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation [J]. Nature Biomedical Engineering, 2017, 1(2): 0025.

AMERI A, AKHAEE M A, SCHEME E, et al. Real-time, simultaneous myoelectric control using a convolutional neural network [J]. PLoS One, 2018, 13(9): e0203835.

LIN Chuang, WANG Binghui, JIANG Ning, et al. Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization [J]. Journal of Neural Engineering, 2018, 15(2): 026017.

AMERI A, AKHAEE M A, SCHEME E, et al. Regression convolutional neural network for improved simultaneous EMG control [J]. Journal of Neural Engineering, 2019, 16(3): 036015.

NIELSEN J L G, HOLMGAARD S, JIANG Ning, et al. Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training [J]. IEEE Transactions on Biomedical Engineering, 2011, 58(3): 681-688.

MUCELI S, FARINA D. Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 20(3): 371-378.

NGEO J G, TAMEI T, SHIBATA T. Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model [J]. Journal of Neuroengineering and Rehabilitation, 2014, 11: 122.

CELADON N, DOEN S, BINDER I, et al. Proportional estimation of finger movements from high-density surface electromyography [J]. Journal of Neuroengineering and Rehabilitation, 2016, 13(1): 73.

XILOYANNIS M, GAVRIEL C, THOMIK A A C, et al. Gaussian process autoregression for simultaneous proportional multi-modal prosthetic control with natural hand kinematics [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(10): 1785-1801.

BAO Tianzhe, ZAIDI S A R, XIE Shengquan, et al. A CNN-LSTM hybrid model for wrist kinematics estimation using surface electromyography [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-9.

KOCH P, DREIER M, LARSEN A, et al. Regression of hand movements from sEMG data with recurrent neural networks [C]∥2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society(EMBC). Piscataway, NJ, USA: IEEE, 2020: 3783-3787.

ZHOU Zhihua, FENG Ji. Deep forest: towards an alternative to deep neural networks [C]∥Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. California, USA: IJCAI, 2017: 3553-3559.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

采用机器学习的大规模并行程序I/O性能建模与预测方法

采用机器学习的天然气掺氢发动机可预测燃烧模型

机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证

利用人工神经网络的偏光下热解炭织构类型识别

面向高海拔空冷发电机防晕性能演化规律的电热耦合研究