Method for sEMG-based Motion Recognition for Patients at Different Brunnstrom Stages
WANG Fengyan1,2,3, ZHANG Daohui1,2, LI Ziyou1,2,3, ZHAO Xingang1,2
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; 2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：Aiming at the problem of a low accuracy of motion intention recognition for stroke patients at different disease degrees, a motion recognition method based on surface electromyography (sEMG) for patients at different Brunnstrom stages is proposed. Firstly, sEMG data of patients at different Brunnstrom stages are fused, and tsfresh library is used for feature extraction. Then, the features are selected based on random forest (RF) model, and the selected features are used for training action classification model. Furthermore, the rehabilitation evaluation actions are determined by studying the relationships between actions and rehabilitation grades, and an automatic evaluation algorithm of rehabilitation grade is designed. In order to verify the effectiveness of the proposed method, the sEMG data of 24 patients are tested. The experimental results show that the proposed method can improve the average recognition accuracy of 9 kinds of actions and 6 types of rehabilitation stages to 89.81% and 94% respectively. The hand rehabilitation robot system based on the proposed method can realize the automatic evaluation of rehabilitation grade.
 Forouzanfar M H, Afshin A, Alexander L T, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015:A systematic analysis for the Global Burden of Disease Study 2015[J]. The Lancet, 2016, 388(10053):1659-1724.  Zhou M, Wang H, Zhu J, et al. Cause-specific mortality for 240 causes in China during 1990-2013:A systematic subnational analysis for the Global Burden of Disease Study 2013[J]. The Lancet, 2016, 387(10015):251-272.  贾杰.脑卒中后手功能康复现状[J].老年医学与保健,2015,21(3):129-131.Jia J. Hand function rehabilitation after stroke[J]. Geriatrics & Health Care, 2015, 21(3):129-131.  Butler A J, Page S J. Mental practice with motor imagery:Evidence for motor recovery and cortical reorganization after stroke[J]. Archives of Physical Medicine and Rehabilitation, 2006, 87(12):2-11.  王启宁, 郑恩昊, 陈保君,等.脑卒中意念控制的主被动运动康复技术[J].机器人, 2017,39(5):759-768.Li M, Xu G H, Xie J, et al. Motor rehabilitation with control based on human intent for stroke survivors[J]. Robot, 2017, 39(5):759-768.  Liparulo L, Zhang Z, Panella M, et al. A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography[J]. Medical & Biological Engineering & Computing, 2017, 55(8):1367-1378.  Kristensen H K, Tistad M, von Koch L, et al. The importance of patient involvement in stroke rehabilitation[J]. PLoS One, 2016, 11(6).  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[J].自动化学报, 2016,42(12):1765-1779.Hou Z G, Zhao X G, Cheng L, et al. Recent advances in rehabilitation robots and intelligent assistance systems[J]. Acta Automatica Sinica, 2016,42(12):1765-1779.  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 Automatica Sinica, 2016, 42(1):13-25.  Shenoy P, Miller K J, Crawford B, et al. Online electromyographic control of a robotic prosthesis[J]. IEEE Transactions on Biomedical Engineering, 2008, 55(3):1128-1135.  Wei W, Wong Y, Du Y, et al. A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface[J]. Pattern Recognition Letters, 2019, 119:131-138.  Tsinganos P, Cornelis B, Cornelis J, et al. Improved gesture recognition based on sEMG signals and TCN[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, USA:IEEE, 2019:1169-1173.  Lee S W, Wilson K M, Lock B A, et al. Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 19(5):558-566.  Zhang X, Zhou P. High-density myoelectric pattern recognition toward improved stroke rehabilitation[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(6):1649-1657.  Zhang Z, Fang Q, Gu X. Objective assessment of upper-limb mobility for poststroke rehabilitation[J]. IEEE Transactions on Biomedical Engineering, 2015, 63(4):859-868.  Fang Q, Mahmoud S S, Gu X, et al. A novel multistandard compliant hand function assessment method using an infrared imaging device[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 23(2):758-765.  de Weerdt W J G, Harrison M A. Measuring recovery of arm-hand function in stroke patients:A comparison of the Brunnstrom-Fugl-Meyer test and the action research arm test[J]. Physiotherapy Canada, 1985, 37(2):65-70.  Sathiyanarayanan M, Rajan S. MYO Armband for physiotherapy healthcare:A case study using gesture recognition application[C]//8th International Conference on Communication Systems and Networks. Piscataway, USA:IEEE, 2016. DOI:10.1109/COMSNETS.2016.7439933.  Folstein M F, Folstein S E, McHugh P R. "Mini-mental state":A practical method for grading the cognitive state of patients for the clinician[J]. Journal of Psychiatric Research, 1975, 12(3):189-198.  Zawawi T, Abdullah A R, Jopri M H, et al. A review of electromyography signal analysis techniques for musculoskeletal disorders[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2018, 11(33):1136-1146.  Christ M, Braun N, Neuffer J, et al. Time series feature extraction on basis of scalable hypothesis tests (tsfresh-A Python package)[J]. Neurocomputing, 2018, 307:72-77.  Venkatesh B, Anuradha J. A review of feature selection and its methods[J]. Cybernetics and Information Technologies, 2019, 19(1):3-26.  Saeys Y, Abeel T, van de Peer Y. Robust feature selection using ensemble feature selection techniques[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Germany:Springer, 2008:313-325.  Yang P, Yang J Y H, Zhou B B, et al. A review of ensemble methods in bioinformatics[J]. Current Bioinformatics, 2010, 5(4):296-308.  Wu J J, Li X O, Liu W Y, et al. sEMG signal processing methods:A review[J]//Journal of Physics:Conference Series, 2019, 1237(3). DOI:10.1088/1742-6596/1237/3/032008.  Johansson B B. Current trends in stroke rehabilitation:A review with focus on brain plasticity[J]. Acta Neurologica Scandinavica, 2011, 123(3):147-159.