Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 12/2019

16-03-2019 | Original Article

A novel feature extraction method for machine learning based on surface electromyography from healthy brain

Authors: Gongfa Li, Jiahan Li, Zhaojie Ju, Ying Sun, Jianyi Kong

Published in: Neural Computing and Applications | Issue 12/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Convolutional neural network for spectral–spatial classification of hyperspectral images
next article Regression trees modeling of time series for air pollution analysis and forecasting

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
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
2.
Castellini C, Smagt P (2009) Surface EMG in advanced hand prosthetics. Biol Cybern 100:35–47
3.
4.
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
5.
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
6.
7.
8.
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):238–247MathSciNetMATH
9.
10.
11.
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
12.
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
13.
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
14.
Scott R (1966) Myoelectric control of prostheses. Arch Phys Med Rehabil 47:174
15.
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
16.
17.
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
18.
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
19.
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
20.
21.
Orosco E, Lopez N, Sciascio F (2013) Bispectrum-based features classification for myoelectric control. Biomed Signal Process Control 8:153–168
22.
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
23.
Hudgins B, Parker P, Scott R (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40:82–94
24.
25.
Chen X, Zhu X, Zhang D (2010) A discriminant bispectrum feature for surface electromyogram signal classification. Med Eng Phys 32:126–135
26.
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
27.
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
28.
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
29.
30.
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
31.
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
32.
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
33.
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
34.
Xinpu C, Xiangyang Z, Dingguo Z (2010) A discriminant bispectrum feature for surface electromyogram signal classification. Med Eng Phys 22:126–135
35.
Subasi A (2012) Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput J 12:2188–2198
36.
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
37.
Yang L, Yantao T, Wanzhong C (2012) Modeling and classifying of sEMG based on FFT blind identification. Acta Automatica Sinica 38(1):128–134MathSciNet
38.
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
39.
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
40.
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
41.
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
42.
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
43.
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
44.
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
45.
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
46.
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):1687814015604546MATH
47.
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
48.
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
49.
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
50.
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
51.
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
52.
53.
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
54.
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
55.
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
56.
Bu N, Hamamoto T, Tsuji T, Fukuda O (2005) FPGA implementation of a probabilistic neural network. IEICE Trans Inf Syst 88:390–397
57.
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
58.
Ahsan M, Ibrahimy M, Khalifa O (2009) EMG signal classification for human computer interaction: a review. Eur J Sci Res 33:480–501
59.
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
60.
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
61.
Chan A, Englehart K (2005) Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Trans Biomed Eng 52:121–124
62.
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
63.
Khezri M, Jahed M (2007) Real-time intelligent pattern recognition algorithm for surface EMG signals. Biomed Eng Online 6:45
64.
Disi C, Gongfa L (2017) An interactive image segmentation method in hand gesture recognition. Sensors 17(2):253
65.
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
66.
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
67.
68.
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):1415–1421MathSciNetMATH
69.
70.
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
71.
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):1223–1237MathSciNetMATH
72.
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):13–24MathSciNetMATH
Metadata
Title
A novel feature extraction method for machine learning based on surface electromyography from healthy brain
Authors
Gongfa Li
Jiahan Li
Zhaojie Ju
Ying Sun
Jianyi Kong
Publication date
16-03-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 12/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04147-3

Other articles of this Issue 12/2019

Neural Computing and Applications 12/2019 Go to the issue

Original Article

Superpixels for large dataset subspace clustering

Original Article

A motion classification model with improved robustness through deformation code integration

Original Article

Belief structure-based induced aggregation operators in decision making with hesitant fuzzy information

Machine Learning - Applications & Techniques in Cyber Intelligence

Research on prediction model of geotechnical parameters based on BP neural network

Original Article

Multi-objective multi-item fixed-charge solid transportation problem under twofold uncertainty

Machine Learning - Applications & Techniques in Cyber Intelligence

Unsupervised learning low-rank tensor from incomplete and grossly corrupted data

Premium Partner

    Image Credits