Abstract
Feature extraction is one of most important steps in the control of multifunctional prosthesis based on surface electromyography (sEMG) pattern recognition. In this paper, a new sEMG feature extraction method based on muscle active region is proposed. This paper designs an experiment to classify four hand motions using different features. This experiment is used to prove that new features have better classification performance. The experimental results show that the new feature, active muscle regions (AMR), has better classification performance than other traditional features, mean absolute value (MAV), waveform length (WL), zero crossing (ZC) and slope sign changes (SSC). The average classification errors of AMR, MAV, WL, ZC and SSC are 13%, 19%, 26%, 24% and 22%, respectively. The new EMG features are based on the mapping relationship between hand movements and forearm active muscle regions. This mapping relationship has been confirmed in medicine. We obtain the active muscle regions data from the original EMG signal by the new feature extraction algorithm. The results obtained from this algorithm can well represent hand motions. On the other hand, the new feature vector size is much smaller than other features. The new feature can narrow the computational cost. This proves that the AMR can improve sEMG pattern recognition accuracy rate.
Similar content being viewed by others
References
Yinfeng F, Hettiarachchi N, Dalin Z, Honghai L (2015) Multi-modal sensing techniques for interfacing hand prostheses: a review. IEEE Sens J 15:6065–6076
Castellini C, Smagt P (2009) Surface EMG in advanced hand prosthetics. Biol Cybern 100:35–47
Wentao C, Wentao S, Gongfa L, Guozhang J, Honghai L (2018) Jointly network: a network based on CNN and RBM for gesture recognition. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3775-8
Arieta A, Yokoi H, Arai T, Yu W (2006) Study on the effects of electrical stimulation on the pattern recognition for an EMG prosthetic application. In: Proc IEEE Int Conf Eng Med Biol Soc, pp 6919–6922
Zhang D, Chen X, Li S, Hu P, Zhu X (2011) EMG controlled multifunctional prosthetic hand: preliminary clinical study and experimental demonstration. In: Proc IEEE Int Conf Robot Autom, pp 4670–4675
Yang H, Gongfa L, Yajie L, Ying S, Jianyi K, Guozhang J, Du J (2017) Gesture recognition based on an improved local sparse representation classification algorithm. Clust Comput. https://doi.org/10.1007/s10586-017-1237-1
Bei L, Ying S, Gongfa L, Jianyi K, Guozhang J, Du J, Honghai L (2017) Gesture recognition based on modified adaptive orthogonal matching pursuit algorithm. Clust Comput. https://doi.org/10.1007/s10586-017-1231-7
Wei M, Gongfa L, Guozhang J, Yinfeng F, Zhaojie J, Honghai L (2015) Optimal grasp planning of multi-fingered robotic hands: a review. Appl Comput Math 14(3):238–247
Gongfa L, Heng T, Ying S, Jianyi K, Guozhang J (2017) Hand gesture recognition based on convolution neural network. Clust Comput. https://doi.org/10.1007/s10586-017-1435-x
Du J, Zujia Z, Gongfa L, Ying S, Jianyi K, Guozhang J, Hegen X, Bo T, Shuang X, Hui Y, Honghai L, Zhaojie J (2018) Gesture recognition based on binocular vision. Clust Comput. https://doi.org/10.1007/s10586-018-1844-5
Ying S, Cuiqiao L, Gongfa L, Guozhang J, Du J, Honghai L, Zhigao Z, Wanneng S (2018) Gesture recognition based on kinect and sEMG signal fusion. Mob Netw Appl. 23(4):797–805
Weiliang D, Gongfa L, Guozhang J, Yinfeng F, Zhaojie J, Honghai L (2015) Intelligent computation in grasping control of dexterous robot hand. J Comput Theor Nanosci 12(12):6096–6099
Zhe L, Gongfa L, Guozhang J, Yinfeng F, Zhaojie J, Honghai L (2015) Intelligent Computation of grasping and manipulation for multi-fingered robotic hands. J Comput Theor Nanosci 12(12):6192–6197
Scott R (1966) Myoelectric control of prostheses. Arch Phys Med Rehabil 47:174
Zhang F, Li P, Hou Z (2012) sEMG-based continuous estimation of joint angles of human legs by using BP neural network. Neurocomputing 78:139–148
Jiang D, Gongfa L, Ying S, Jianyi K, Bo T (2018) Gesture recognition based on skeletonization algorithm and CNN with ASL database. Multimedia Tools Appl. https://doi.org/10.1007/s11042-018-6748-0
Lee J, Kim M, Kim K (2015) A robust control method of multi-DOF power-assistant robots for unknown external perturbation using sEMG signals. In: IEEE/RSJ international conference on intelligent robots and systems (Iros), pp 1045–1051
Jianda H, Qichuan D, Anbin X, Xingang Z (2015) A state-space EMG model for the estimation of continuous joint movements. IEEE Trans Industr Electron 62:4267–4275
Daley H, Englehart K, Hargrove L, Kuruganti U (2015) High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. Electromyogr Kinesol 22:478–484
Chengcheng L, Gongfa L, Guozhang J, Disi C, Honghai L (2018) Surface EMG data aggregation processing for intelligent prosthetic action recognition. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3909-z
Orosco E, Lopez N, Sciascio F (2013) Bispectrum-based features classification for myoelectric control. Biomed Signal Process Control 8:153–168
Angkoon P, Franck Q, Sylvie C, Christine S, Frank T, Yann L (2013) EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst Appl 40:4832–4840
Hudgins B, Parker P, Scott R (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40:82–94
Bowen L, Ying S, Gongfa L, Disi C, Zhaojie J (2019) Decomposition Algorithm for Depth Image of Human Health Posture Based on Brain Health. Neural comput Appl. https://doi.org/10.1007/s00521-019-04141-9
Chen X, Zhu X, Zhang D (2010) A discriminant bispectrum feature for surface electromyogram signal classification. Med Eng Phys 32:126–135
Graupe D, Salahi J, Zhang D (1985) Stochastic analysis of myoelectric temporal signatures for multifunctional single-site activation of prostheses and orthoses. J Biomed Eng 7:18–29
Yinfeng F, Honghai L, Gongfa L, Xiangyang Z (2015) A multichannel surface EMG system for hand motion recognition. Int J Humanoid Rob 12:381–509
Young A, Hargrove L, Kuiken T (2012) Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Trans Biomed Eng 59:645–652
Chong T, Ying S, Gongfa L, Guozhang J, Disi C, Honghai L (2019) Research on Gesture Recognition of Smart Data Fusion Features in the IoT. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04023-0
Zhaojie J, Gaoxiang O, Korsak W, Honghai L (2013) Surface EMG based hand manipulation identification via nonlinear feature extraction and classification. IEEE Sens J 13:3302–3311
Poosapadi A, Kumar D (2014) Computation of fractal features based on the fractal analysis of surface electromyogram to estimate force of contraction of different muscles. Comput Methods Biomech Biomed Eng 17:210–216
Ancillao A, Galli M, Rigoldi C, Albertini G (2014) Linear correlation between fractal dimension of surface EMG signal from Rectus Femoris and height of vertical jump. Chaos Solitons Fractals 66:120–126
Gongfa L, Yuesheng G, Jianyi K, Guozhang J, Liangxi X, Zehao W, Zhen L, Yuan H, Po G (2013) Intelligent control of air compressor production process. Appl Math Inf Sci 7(3):1051–1058
Xinpu C, Xiangyang Z, Dingguo Z (2010) A discriminant bispectrum feature for surface electromyogram signal classification. Med Eng Phys 22:126–135
Subasi A (2012) Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput J 12:2188–2198
Lukai L, Pu L, Edward A (2013) Electromyogram whitening for improved classification accuracy in upper limb prosthesis control. IEEE Trans Neural Syst Rehabil Eng 21:767–774
Yang L, Yantao T, Wanzhong C (2012) Modeling and classifying of sEMG based on FFT blind identification. Acta Automatica Sinica 38(1):128–134
Gongfa L, Peixin Q, Jianyi K, Guozhang J, Liangxi X, Po G, Zehao W, Yuan H (2013) Coke oven intelligent integrated control system. Appl Math Inf Sci 7(3):1043–1050
Gang W, Zhizhong W, Weiting C, Jun Z (2006) Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Med Biol Eng Comput 44(10):865–872
Wenjun C, Gongfa L, Jianyi K, Ying S, Guozhang J, Honghai L (2018) Thermal mechanical stress analysis of ladle lining with integral brick joint. Arch Metall Mater 63:659–666
Andrews A, Morin E, Mclean L (2009) Optimal electrode configurations for finger movement classification using EMG. In: Engineering in medicine and biology society, pp 2987–2990
Hargrove L, Englehart K, Hudgins B (2008) A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed Signal Process Control 3(2):175–180
Gongfa L, Peixin Q, Jianyi K, Guozhang J, Liangxi X, Zehao W, Po G, Yuan H (2013) Influence of working lining parameters on temperature and stress field of ladle. Appl Math Inf Sci 7(2):439–448
Hargrove L, Englehart K, Hudgins B (2006) The effect of electrode displacements on pattern recognition based myoelectric control. Conf Proc IEEE Eng Med Biol Soc 1:2203–2206
Mojtaba M, Farbod R, ShMahdi A (2010) Elimination of power line noise from EMG signals using an efficient adaptive Laguerre filter. In: IEEE international conference on signals and electronic systems, pp 49–52
Hegen X, Huali F, Gongfa L, Guozhang J (2015) Research on steady-state simulation in dynamic job shop scheduling problem. Adv Mech Eng 7(9):1687814015604546
Guanglin L, Yanjuan G, Dandan T, Ping Z (2011) Performance of electromyography recorded using textile electrodes in classifying arm movements. In: Conf Proc IEEE Eng Med Biol Soc, pp 4243–4246
Carlo L, Donald Gilmore L, Mikhail K, Serge B (2010) Filtering the surface EMG signal: movement artifact and baseline noise contamination. J Biomech 43:1573–1579
Dapeng Y, Jingdong Z, Jiang L, Hong L (2012) Dynamic hand motion recognition based on transient and steady-state EMG signals. Int J Humanoid Rob 9:1250–1256
Shenkai C, Mingtung W, Chunhao H, Jiahroung W, Lanyuen G, Wenlan W (2013) The analysis of upper limb movement and EMG activation during the snatch under various loading conditions. J Mech Med Biol 13:1350–1357
Md A, Muhammad I, Othman K (2011) Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN). In: 4th international conference on mechatronics, Malaysia, vol 4, pp 1–6
Jinxian Q, Guozhang J, Gongfa L, Ying S, Bo T (2019) Surface EMG Hand Gesture Recognition System Based on PCA and GRNN. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04142-8
Gongfa L, Hao W, Guozhang J, Shuang X, Honghai L (2019) Dynamic Gesture Recognition in the Internet of Things. IEEE Access 7(1):23713–23724
Gongfa L, Ze L, Guozhang J, Hegen X, Honghai L (2015) Numerical simulation of the influence factors for rotary kiln in temperature field and stress field and the structure optimization. Adv Mech Eng 7(6):1687814015589667
Veternik M, Simera M, Jakus J, Poliacek I (2013) Integration of simulated multipotential signals: the role of integration window width and of the number of spikes. Neurobiol Respir 788:265–272
Bu N, Hamamoto T, Tsuji T, Fukuda O (2005) FPGA implementation of a probabilistic neural network. IEICE Trans Inf Syst 88:390–397
Gongfa L, Jianyi K, Guozhang J, Liangxi X, Zhigang J, Gang Z (2012) Air-fuel ratio intelligent control in coke oven combustion process. Inf Int Interdiscip J 15:4487–4494
Ahsan M, Ibrahimy M, Khalifa O (2009) EMG signal classification for human computer interaction: a review. Eur J Sci Res 33:480–501
Gongfa L, Du J, Yanling Z, Guozhang J, Jianyi K (2019) Gunasekaran Manogaran. Human Lesion Detection Method Based on Image Information and Brain Signal. IEEE Access 7:11533–11542
Gongfa L, Jia L, Guozhang J, Honghai L (2015) Numerical simulation of temperature field and thermal stress field in the new type of ladle with the nanometer adiabatic material. Adv Mech Eng 7(4):1687814015575988
Chan A, Englehart K (2005) Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Trans Biomed Eng 52:121–124
Huang Y, Englehart K, Hudgins B, Chan A (2005) A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans Biomed Eng 52:1801–1811
Khezri M, Jahed M (2007) Real-time intelligent pattern recognition algorithm for surface EMG signals. Biomed Eng Online 6:45
Disi C, Gongfa L (2017) An interactive image segmentation method in hand gesture recognition. Sensors 17(2):253
Yajie L, Ying S, Gongfa L, Jianyi K (2017) Simultaneous calibration: a joint optimization approach for multiple kinect and external cameras. Sensors 17(7):1491
Disi C, Gongfa L, Ying S, Guozhang J, Jianyi K, Jiahan L, Honghai L (2017) Fusion hand gesture segmentation and extraction based on CMOS sensor and 3D sensor. Int J Wirel Mob Comput 12(3):305–312
Ying S, Jiabing H (2018) Practice teaching of mechanical design based on computer media simulation. J Supercomput. https://doi.org/10.1007/s11227-018-2255-3
Qian Y, Gongfa Li, Jiangguo Z (2015) Research on the method of step feature extraction for EOD robot based on 2D laser radar. Discret Contin Dyn Syst Ser 8(6):1415–1421
Gongfa L, Leilei Z, Ying S, Jianyi K (2018) Internet of Things sensors and haptic feedback for sEMG based hands. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6293-x
Yang H, Gongfa L, Yaping Z, Ying S, Guozhang J (2018) Numerical simulation-based optimization of contact stress distribution and lubrication conditions in the straight worm drive. Strength Mater 50(1):157–165
Gongfa L, Wei M, Guozhang J, Yinfeng F, Zhaojie J, Honghai L (2015) Intelligent control model and its simulation of flue temperature in coke oven. Discret Contin Dyn Syst Ser S (DCDS-S) 8(6):1223–1237
Hegen X, Huali F, Guozhang J, Gongfa L (2017) A simulation -based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints. Eur J Oper Res 257(1):13–24
Acknowledgements
This work was supported by grants of Natural Science Foundation of China (Grant Nos. 51575407, 51575338, 51575412, 61733011), Grants of National Natural Science Foundation of China (Grant Nos. 51505349, 51575407, 51575338, 51575412, 61733011) and the Grants of National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705). This paper is funded by Wuhan University of Science and Technology graduate students short-term study abroad special funds.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, G., Li, J., Ju, Z. et al. A novel feature extraction method for machine learning based on surface electromyography from healthy brain. Neural Comput & Applic 31, 9013–9022 (2019). https://doi.org/10.1007/s00521-019-04147-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-019-04147-3