基于表面肌电图的人体运动意图识别研究进展

doi:10.3969/j.issn.1006-9771.2021.05.013

Abstract

Abstract: Objective

To summarize the methods and results of human motion intention recognition based on the surface electromyography.

Methods

Literatures were retrieved and reviewed from the databases of PubMed, Web of Science, CNKI, Wanfang and VIP until December, 2020. The experimental researches about human motion intention recognition based on surface electromyography were summarized.

Results

The methods of motion intention recognition were divided into three models: musculoskeletal model, traditional machine learning model and deep learning model.

Conclusion

It is difficult to fully estimate human motion intention using surface electromyography in a single way. More researches are needed to develop more accurate and real-time human motion intention recognition methods.

Key words: rehabilitation robot, human-machine interaction, surface electromyography, motion intention recognition, review

CLC Number:

R496

Meng-lin CAO,Yu-hao CHEN,Jue WANG,Tian LIU. Advance in Human Motion Intention Recognition Based on Surface Electromyography (review)[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(5): 595-603.

Figures/Tables 5

References 48

1	SINGH R M, CHATTERJI S, KUMAR A. A review on surface EMG based control schemes of exoskeleton robot in stroke rehabilitation [C]. International Conference on Machine Intelligence & Research Advancement, IEEE (ICMIRA), 2013: 310-315.
2	GOPURA R A R C, BANDARA D S V, KIGUCHI K, et al. Developments in hardware systems of active upper-limb exoskeleton robots: a review [J]. Robot Auton Syst, 2016, 75: 203-220.
3	HUO W, MOHAMMED S, MORENO J C, et al. Lower limb wearable robots for assistance and rehabilitation: a state of the art [J]. IEEE Syst J, 2014, 10(3): 1068-1081.
4	MASAYUKI Y, RYOHEI K, MASAO Y. An evaluation of hand-force prediction using artificial neural-network regression models of surface emg signals for handwear devices [J]. J Sens, 2017, 2017: 1-12.
5	ARTEMIADIS P. EMG-based robot control interfaces: past, present and future [J]. Adv Robot Autom, 2012, 1(2): 1-3.
6	SARTORI M, REGGIANI M, FARINA D, et al. EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity [J]. PLoS One, 2012, 7(12): e52618.
7	XIA P, HU J, PENG Y. EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks [J]. Artif Organs, 2018, 42(5): E67-E77.
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[J]. 自动化学报, 2016, 42(1): 13-25.
	DING Q C, XIONG A B, ZHAO X G, et al. A review on researches and applications of sEMG-based motion intent recognition methods [J]. Acta Autom Sin, 2016, 42(1): 13-25.
9	SHEN H, SONG Q, DENG X, et al. Recognition of phases in sit-to-stand motion by Neural Network Ensemble (NNE) for power assist robot [C]. IEEE International Conference on Robotics and Biomimetics (ROBIO), 2007: 1703-1708.
10	KARAVAS N, AJOUDANI A, TSAGARAKIS N, et al. Tele-impedance based assistive control for a compliant knee exoskeleton [J]. Robot Auton Syst, 2015, 73: 78-90.
11	LLOYD D G, BESIER T F. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo [J]. J Biomech, 2003, 36(6): 765-776.
12	HAN J, DING Q, XIONG A, et al. A state-space EMG model for the estimation of continuous joint movements [J]. IEEE Trans Ind Electron, 2015, 62(7): 4267-4275.
13	DING Q, XIONG A, ZHAO X, et al. A novel EMG-driven state space model for the estimation of continuous joint movements [C]. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2011: 2891-2897.
14	陈江城,张小栋,李睿,等. 利用表面肌电信号的下肢动态关节力矩预测模型[J]. 西安交通大学学报, 2015, 49(12): 26-33.
	CHEN J C, ZHANG X D, LI R, et al. Prediction model for dynamic joint torque of lower limb with surface EMG [J]. Acad J Xi'an Jiaotong Univ, 2015, 49(12): 26-33.
15	郭浩. 基于 EMG 的人体下肢运动意图感知与预测研究[D]. 哈尔滨:哈尔滨工业大学, 2019.
	GUO H. Research on human lower limb motion intention detection and prediction based on EMG [D]. Harbin: Harbin Institute of Technology, 2019.
16	PENG F, PENG W, ZHANG C. Evaluation of sEMG-based feature extraction and effective classification method for gait phase detection [C]. Cognitive Systems and Signal Processing, 2018: 138-149.
17	NAIK G R, SELVAN S E, ARJUNAN S P, et al. An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology [J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(3): 675-686.
18	曹祥红,刘磊,杨鹏,等. 利用多源信息和极限学习机的人体运动意图识别[J]. 传感技术学报, 2017, 30(8): 1171-1177.
	CAO X H, LIU L, YANG P, et al. The research of locomotion-mode recognition based on multi-source information and extreme learning machine [J]. Chin J Sens Actuators, 2017, 30(8): 1171-1177.
19	CAI S, CHEN Y, HUANG S, et al. SVM-based classification of sEMG signals for upper-limb self-rehabilitation training [J]. Front Neurorobot, 2019, 13: 31.
20	郑潇. 肌电信号多类特征分析及在步态识别中的应用[D]. 杭州:杭州电子科技大学, 2016.
	ZHENG X. Analysis of multi-features and application of gait recognition based on electromyographic signals [D]. Hangzhou: Hangzhou Dianzi University, 2016.
21	WEI P, XIE R, TANG R, et al. sEMG based gait phase recognition for children with spastic cerebral palsy [J]. Ann Biomed Eng, 2019, 47(1): 223-230.
22	BARBERI F, APRIGLIANO F, GRUPPIONI E, et al. Fast online decoding of motor tasks with single sEMG electrode in lower limb amputees [C]. International Symposium on Wearable Robotics (WeRob), 2018: 110-114.
23	XIE P, CHEN X, MA P P, et al. Identification method of human movement intention based on the fusion feature of EEG and EMG [J]. Proc World Congr Eng, 2013, 2205(1): 1340-1344.
24	ASTUDILLO F, CHARRY J, MINCHALA I, et al. Lower limbs motion intention detection by using pattern recognition [C]. IEEE Third Ecuador Technical Chapters Meeting (ETCM), 2018: 1-6.
25	MORBIDONI C, CUCCHIARELLI A, FIORETTI S, et al. A deep learning approach to EMG-based classification of gait phases during level ground walking [J]. Electronics, 2019, 8(8): 894-908.
26	KARANTARAT O, KITJAIDURE Y. The walking assistance system using the lower limb exoskeleton suit commanded by backpropagation neural network [C]. 11th Biomedical Engineering International Conference (BMEiCON), 2018: 1-5.
27	LUH G C, CAI J J, LEE Y S. Estimation of elbow motion intension under varing weight in lifting movement using an EMG-angle neural network model [C]. International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, 2017: 640-645.
28	ZHANG F, LI P, HOU Z, et al. sEMG-based continuous estimation of joint angles of human legs by using BP neural network [J]. Neurocomputing, 2012, 78(1): 139-148.
29	XIE H, LI G, ZHAO X, et al. Prediction of limb joint angles based on multi-source signals by GS-GRNN for exoskeleton wearer [J]. Sensors, 2020, 20(4): 1104.
30	CHANDRAPAL M, CHEN X, WANG W, et al. Investigating improvements to neural network based EMG to joint torque estimation [J]. Paladyn J Behav Rob, 2011, 2(4): 185-192.
31	SHI Y, WANG S, LI J, et al. Prediction of continuous motion for lower limb joints based on sEMG signal [C]. IEEE International Conference on Mechatronics and Automation (ICMA), 2020: 383-388.
32	PARK K H, LEE S W. Movement intention decoding based on deep learning for multiuser myoelectric interfaces [C]. 4th International Winter Conference on Brain-Computer Interface (BCI), IEEE, 2016: 1-2.
33	CÔTÉ-ALLARD U, FALL C L, Drouin A, et al. Deep learning for electromyographic hand gesture signal classification using transfer learning [J]. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(4): 760-771.
34	WEI W, DAI Q, WONG Y, et al. Surface-electromyography-based gesture recognition by multi-view deep learning [J]. IEEE Trans Biomed Eng, 2019, 66(10): 2964-2973.
35	BU N, FUKUDA O, TSUJI T. EMG-based motion discrimination using a novel recurrent neural network [J]. J Intell Inf Syst, 2003, 21(2): 113-126.
36	SONG J, ZHU A, TU Y, et al. Effects of different feature parameters of sEMG on human motion pattern recognition using multilayer perceptrons and LSTM neural networks [J]. Appl Sci, 2020, 10(10): 3358.
37	CHENG H R, CAO G Z, LI C H, et al. A CNN-LSTM hybrid model for ankle joint motion recognition method based on sEMG [C]. 17th International Conference on Ubiquitous Robots (UR), 2020: 339-344.
38	BETTHAUSER J L, KRALL J T, KALIKI R R, et al. Stable electromyographic sequence prediction during movement transitions using temporal convolutional networks [C]. 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019: 1046-1049.
39	BAO T, ZAIDI A, XIE S, et al. Surface-EMG based wrist kinematics estimation using convolutional neural network [C]. IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2019: 1-4.
40	RANE L, DING Z, MCGREGOR A H, et al. Deep learning for musculoskeletal force prediction [J]. Ann Biomed Eng, 2019, 47(3): 778-789.
41	AMERI A, AKHAEE M A, SCHEME E, et al. Regression convolutional neural network for improved simultaneous EMG control [J]. J Neural Eng, 2019, 16(3): 036015.
42	YANG W, YANG D, LIU Y, et al. Decoding simultaneous multi-DOF wrist movements from raw EMG signals using a convolutional neural network [J]. IEEE Trans Hum-Mach Syst, 2019, 49(5): 411-420.
43	DAO T T. From deep learning to transfer learning for the prediction of skeletal muscle forces [J]. Med Biol Eng Comput, 2019, 57(5): 1049-1058.
44	MA X, LIU Y, SONG Q, et al. Continuous estimation of knee joint angle based on surface electromyography using a long short-term memory neural network and time-advanced feature [J]. Sensors, 2020, 20(17): 4966.
45	MA C, LIN C, SAMUEL O W, et al. Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach [J]. Biomed Signal Process Control, 2020, 61: 102024.
46	XIA P, HU J, PENG Y. EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks [J]. Artif Organs, 2018, 42(5): E67-E77.
47	XU L, CHEN X, CAO S, et al. Feasibility study of advanced neural networks applied to sEMG-based force estimation [J]. Sensors (Basel), 2018, 18(10): 3226-3241.
48	GAUTAM A, PANWAR M, BISWAS D, et al. MyoNet: A transfer-learning-based LRCN for lower limb movement recognition and knee joint angle prediction for remote monitoring of rehabilitation progress from sEMG [J]. IEEE J Transl Eng Health Med, 2020, 8: 1-10.

作者	年份	对象	动作	提取特征	方法	时间	准确率
Xie等^[23]	2013	脑卒中、周围神经损伤、健康人	伸膝/屈膝	有效PF的多尺度熵	ELM	—	80.5% (脑卒中) 79.6% (周围神经损伤) 93.4% (健康人)
郑潇^[20]	2016	健康人	支撑前期/中期/后期/摆动前期/后期	MAV和VAR(SVM)；非线性分形维数(K-Means)	SVM、改进K-Means	—	SVM > 95% K-Means 92.1%
Barberi等^[22]	2018	截肢者	步行/上坡/下坡/上下楼梯	MFL	LDA	< 100 ms	100%
Wei等^[21]	2018	痉挛性脑瘫患儿	站立中期/站立末期/摆动前期/摆动中期/摆动末期	多种时域或频域特征的组合，最终选取MAV和ZC	SVM	—	89.40%
Peng等^[16]	2018	健康男性	站立前态/中间态/末态/前摆/中摆/终摆	SSC、WL、Wamp、Logvar、DB7-MAV	SV-LDA、WT-LDA、XGBoost、LightGBM	85 ms	94.3% (LightGBM)
Karantarat等^[26]	2018	患者、健康人	步行/坐着/站立	时域特征	BPNN	—	99.39%
Astudillo等^[24]	2018	健康人	两种动作	RMS、导数	ANN	(12.6±10) ms	94.88%
Morbidoni等^[25]	2019	健康人	站立/摆动	EMG信号的包络线	MLP	—	93.4%

作者	年份	对象	回归目标	方法	结果
Zhang等^[28]	2012	健康人	脚踝、膝盖、臀部关节角度	BPNN	平均误差< 9°
Luh等^[27]	2017	健康人	肘关节角度	BPNN	有较高的精度
Chandrapal等^[30]	2011	健康人	膝关节扭矩	MLP	平均最低估计误差10.46%
Masayuki等^[4]	2017	健康人	握力	ANN	平均CC = 0.84
Xie等^[29]	2020	—	髋、膝、踝关节角度	GS-GRNN	髋CC = 0.9985，膝CC = 0.9925，踝CC = 0.9873

作者	年份	研究对象	分类动作	网络输入	分类方法	时间	准确率
Bu等^[35]	2003	健康人	6种手部运动	原始EMG	递归对数线性化高斯混合网络	—	88.4%
Park等^[32]	2016	健康人	6种手部运动	原始EMG	CNN	—	90%
C?té-Allard等^[33]	2019	健康人	手势识别	原始EMG，频谱图，CWT	ConvNet	—	98.31%
Wei等^[21]	2019	11个数据库，部分数据库中有截肢者	手势识别	通过穷举，找到3种特征，输入网络	multi-view深度学习框架	—	较高的识别精度
Song等^[36]	2020	健康人	7种常见运动模式	时域、频域特征	MLP、LSTM	—	MLP 95.53%， LSTM 96.57%
Cheng等^[37]	2020	—	4种踝关节动作	原始EMG	CNN-LSTM	—	97.55%±1.93%
Betthauser等^[38]	2019	—	3种手部运动	MAV	TCN	—	准确率与LSTM相当，稳定性高与LSTM

作者	年份	研究对象	回归目标	网络输入	回归方法	时间	回归精度
Bao等^[39]	2019	健康人	腕关节角度	原始EMG信号、时域、频谱	CNN	—	频谱图效果最好
Rane等^[40]	2019	健康人、骨关节炎患者	骨骼肌力	原始EMG信号	CNN	71 ms	结果优于比赛中6个获奖作品中的4个
Ameri等^[41]	2019	健康人	关节自由度	EMG信号矩阵	CNN	6 ms	误差< 10%
Yang等^[42]	2019	健康人	关节自由度	EMG信号	CNN	比SVM快	比SVM模型准确
Dao^[43]	2019	健康人	骨骼肌力	原始EMG信号	LSTM、权重转移策略	—	RMSE < 10%， CC = 0.95~0.99
Xia等^[46]	2018	健康人	上肢运动轨迹	时频信号	RCNN	—	平均CC 0.903
Xu等^[47]	2018	健康人	上肢力	NMF信号提取	CNN、LSTM、CNN-LSTM	—	平均RMSE(%) CNN：12.13±1.98 LSTM：9.07±1.29 CNN-LSTM：8.67±1.14
Ma等^[44]	2020	健康人	膝关节角度	sEMG的RMS及其时间提前特征	LSTM	—	平均RMSE (3.4726±0.6162)°
Ma等^[45]	2020	健康人	上肢关节角度	EMG信号包络	SCA-LSTM	—	平均CC (0.957±0.013)
Gautam等^[48]	2020	健康人和膝关节病患者	膝关节角度	EMG信号	LRCN	—	MAE：健康人为8.1%，患者为9.2%

[1]	SHAO Weiting, LEI Jianghua. Effect of response interruption and redirection as a behavioral intervention on vocal stereotypy in children with autism spectrum disorder: a scoping review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2024, 30(1): 10-20.
[2]	WANG Hangyu, GE Keke, FAN Yonghong, DU Lilu, ZOU Min, FENG Lei. Effect of active music therapy on cognitive function for older adults with cognitive impairment: a systematic review based on ICD-11 and ICF [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2024, 30(1): 36-43.
[3]	WEN Jianing, JIN Qiuyan, ZHANG Qi, LI Jie, SI Qi. Effect of cognitively engaging physical activity on developing executive function of children and adolescents: a systematic review based on ICF [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2024, 30(1): 44-53.
[4]	GE Keke, FAN Yonghong, WANG Hangyu, DU Lilu, LI Changjiang, ZOU Min. Health benefit of mindfulness intervention for older adults with insomnia disorders: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2024, 30(1): 54-60.
[5]	ZHANG Jingya, ZOU Min, SUN Hongwei, SUN Changlong, ZHU Juntong. Effect of psychological intervention on anxiety or depression in children and adolescents with hearing impairment: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(9): 1004-1011.
[6]	WANG Junyu, YANG Yong, YUAN Xun, XIE Ting, ZHUANG Jie. Effect of high-intensity interval training on executive function for healthy children and adolescents: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(9): 1012-1020.
[7]	WEI Xiaowei, YANG Jian, WEI Chunyan. Psychological and behavioral benefits of adapted yoga exercise for children with autism spectrum disorder in special education schools: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(9): 1021-1028.
[8]	YANG Yaru, YANG Jian. School-based physical activity-related health services and their health benefits within the World Health Organization health-promoting school framework: a systematic review of systematic reviews [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(9): 1040-1047.
[9]	WANG He, HAN Liang, KAN Mengfan, YU Shaohong. Efficacy of electrical stimulation on shoulder-hand syndrome after stroke: a systematic review and meta-analysis [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(9): 1048-1056.
[10]	SHI Jiawei, LI Lingyu, YANG Haojie, WANG Qinlu, ZOU Haiou. Effect of preoperative prerehabilitation training on total knee arthroplasty: a systematic review of systematic reviews [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(9): 1057-1064.
[11]	MA Shengnan, KE Jingyue, DONG Hongming, LI Jianping, ZHANG Honghao, LIU Chao, SHEN Shuang, LI Guqiang. Effect of core stability training on dynamic balance and surface electromyography after anterior cruciate ligament reconstruction [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(8): 882-889.
[12]	JIANG Changhao, HUANG Chen, GAO Xiaoyan, DAI Yuanfu, ZHAO Guoming. Effect of neurofeedback training on cognitive function in the elderly: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(8): 903-909.
[13]	WEI Xiaowei, YANG Jian, WEI Chunyan, HE Qiling. Adapted physical education programs for psychomotor development in school settings for children with intellectual and developmental disabilities: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(8): 910-918.
[14]	CUI Yao, CONG Fang, HUANG Fubiao, ZENG Ming, YAN Ruxiu. Brain and muscle activation under mirror neuron-based training strategies: a near-infrared spectroscopy and surface electromyography study [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(7): 782-790.
[15]	ZHANG Yuan, YANG Jian. School health services and effectiveness based on World Health Organization health-promoting school framework: a scoping review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(7): 791-799.

Advance in Human Motion Intention Recognition Based on Surface Electromyography (review)

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 48

Related Articles 15

Metrics

Comments

Recommended 0