Skip to main content
Springer Nature Link
Log in

Efficient deep neural network model for classification of grasp types using sEMG signals

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Grasping is a challenging problem in robotics and prosthetic applications due to its control requirements. The visual perception and analyzing electromyography (EMG) signals are the two ways to give the inputs to robots and prosthetic amputees for grasping abilities. The EMG is a diagnostic manner that evaluates the fitness condition of skeletal muscles. Examination or evaluation of the EMG signals is time-consuming and arduous for experts. Hence, the state-of-the-art methods in artificial intelligence (AI) is employed to improve the accuracy rate for the detection and classification of EMG signals for grasping. Recently, deep learning architectures have been used in many engineering applications such as diagnosis of health conditions, computer vision, and human machine interaction (HMI). In this study, a new deep one-dimensional convolutional neural network model (1D-CNN) is proposed to classify six types of hand movements. Our proposed 1D-CNN model implemented using surface EMG (sEMG) has obtained the highest accuracy of 94.94% in classifying six hand movements. The strength of our model is that, it can perform the automated classification of various hard grasps using only one channel data. Our developed prototype model is ready to be tested with more data and can be used to assist in musculoskeletal disorders.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ahsan MR, Ibrahimy MI, Khalif OO (2010) Advances in electromyogram signal classification to improve the quality of life for the disabled and aged people. J Comput Sci 6(7):705–715

    Article  Google Scholar 

  • Akben S B (2017) Low-cost and easy-to-use grasp classification, using a simple 2-channel surface electromyography (sEMG) Biomedical Research Volume 28, Issue 2

  • Al-Timemy AH, Khushaba RN, Bugmann G, Escudero J (2015) Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans Neural Syst Rehabil Eng 24(6):650–661

    Article  Google Scholar 

  • Arel I, Rose DC, Karnowski TP (2010) Deep machine learning-a new frontier in artificial intelligence research IEEE Comput. Intell Mag 5(4):13–18

    Article  Google Scholar 

  • Bengio Y (2009) Learning deep architectures for AI Foundations and trends in Machine. Learning 2(1):1–127. https://doi.org/10.1561/2200000006

    Article  MathSciNet  MATH  Google Scholar 

  • Bitzer S, van der Smagt P (2006) Learning EMG control of a robotic hand: towards active prostheses. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006. ICRA 2006, Orlando, FL, pp. 2819–2823

  • Carroll D, Subbiah A (2012) Recent advances in biosensors and biosensing protocols. J Biosens Bioelectron 3:3. https://doi.org/10.4172/2155-6210.1000e112

    Article  Google Scholar 

  • Celik Y, Talo M, Yildirim O, Karabatak M, Acharya UR (2020) Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recogn Lett 133:232–239

    Article  Google Scholar 

  • Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25:24–29

    Article  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  MathSciNet  Google Scholar 

  • Geron A (2017) Hands-On Machine Learning with Scikit-Learn and TensorFlow, pages 82–93

  • Ginia G, Arvettia M, Somlaib I, Folgheraiter M (2012) Acquisition and analysis of EMG signals to recognize multiple hand movements for prosthetic applications. Appl Bionics Biomech 9:145–155

    Article  Google Scholar 

  • Goodfellow I, Bengio Y, Courville (2016) A Deep Learning Book in preparation for MIT Press

  • Iqbal O, Fattah SA, Zahin S (2017) Hand movement recognition based on singular value decomposition of surface EMG signal, In Humanitarian Technology Conference (R10-HTC). IEEE Region 10:837–842

    Google Scholar 

  • Ju Z, Liu H (2014) Human hand motion analysis with multisensory information. IEEE/ASME Trans Mechatron 19:456–466

    Article  Google Scholar 

  • Kakoty N M, Hazarika S M (2011) Recognition of grasp types through principal components of DWT based EMG features. In: Proceedings of IEEE Int. conference on rehabilitation Robotics pp.1–6

  • Kayabasi A, Yildiz B, Aslan M F, Durdu A (2018) Comparison of ELM and ANN on EMG signals obtained for control of robotic-hand. In: 10th international conference on electronics, computers and artificial intelligence (ECAI), Iasi, Romania pp. 1–5

  • Khezri M, Jahed M (2008) Surface electromyogram signal estimation based on wavelet thresholding technique. In: Proceedings of 30th annual international conference of the IEEE engineering in medicine and biology society pp. 4752–4755

  • Khushaba RN, Al-Timemy A, Kodagoda S, Nazarpour K (2016) Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst Appl 61:154–161

    Article  Google Scholar 

  • Kim S, Kim J, Kim M, Kim S, Park J (2019) Grasping force estimation by sEMG signals and arm posture: tensor decomposition approach. J Bionic Eng 16(3):455–467

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1–9

    Google Scholar 

  • Li K, Fang Y, Zhou Y, Liu H (2017) Non-invasive stimulation-based tactile sensation for upper-extremity prosthesis: a review. IEEE Sensors J 17(9):2625–2635

    Article  Google Scholar 

  • Li G, Zhang L, Sun Y, Kong J (2018) Towards the sEMG hand: internet of things sensors and haptic feedback application. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6293-x

    Article  Google Scholar 

  • Li GF, Wu H, Jiang GZ, Xu S, Liu HH (2019) Dynamic gesture recognition in the internet of things. IEEE Access 7:23713–23724

    Article  Google Scholar 

  • Murat F, Yildirim O, Talo M, Baloglu UB, Demir Y, Acharya UR (2020) Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput Biol Med 120:103726

    Article  Google Scholar 

  • Nishad A, Upadhyay A, Pachori RB, Acharya UR (2019) Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals. Futur Gener Comput Syst 93:96–110

    Article  Google Scholar 

  • Ouyang G, Zhu X, Ju Z (2014) Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot. IEEE J Biomed Health Inform 18:257–265

    Article  Google Scholar 

  • Ozturk T, Talo M, Yildirim E A, Baloglu U B, Yildirim O, Acharya U R (2020) Automated detection of covid-19 cases using deep neural networks with x-ray images, Comput. Biol. Med. p. 103792

  • Padmanabhan P, Puthusserypady S (2004) Nonlinear Analysis of EMG Signals—a chaotic approach. In: Proceedings of the 26th annual international conference of the IEEE engineering in medicine and biology society, San Francisco, CA, USA, Volume 1, pp. 608–611

  • Pal A, Gautam AK, Singh YN (2015) Evaluation of bioelectric signals for human recognition. Proc Comput Sci 48:747–753

    Article  Google Scholar 

  • Phinyomark A, Scheme E (2018) EMG pattern recognition in the era of big data and deep learning. Big Data Cognitive Comput No 3, c. 21 https://doi.org/https://doi.org/10.3390/bdcc2030021

  • Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y (2013) EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst Appl 40:4832–4840

    Article  Google Scholar 

  • Qi JX, Jiang GZ, Li G, Sun Y, Tao B (2019b) Intelligent human-computer interaction based on surface EMG gesture recognition. IEEE Access 7:61378–61387

    Article  Google Scholar 

  • Qi J, Jiang G, Li G, Sun Y, Tao B (2019) Surface EMG hand gesture recognition system based on PCA and GRNN. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04142-8

  • Ruangpaisarn Y, Jaiy S (2015) sEMG signal classification using SMO algorithm and singular value decomposition. In: Proceedings of 7th international conference on information technology and electrical engineering pp. 46–50

  • Ruonala V, Pekkonen E, Rissanen S, Airaksinen O, Miroshnichenko G, Kankaanpää M, Karjalainen P (2014) Dynamic tension EMG to characterize the effects of DBS treatment of advanced Parkinson's disease. In: 36th annual international conference of the IEEE engineering in medicine and biology society, Chicago, IL, pp. 3248–3251

  • Sapsanis C (2013) Recognition of basic hand movements using electromyography, Diploma Thesis, University of Patras

  • Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D (2013) Improving EMG based classification of basic hand movements using EMD. In: 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Osaka, pp. 5754–5757

  • Scheme E, Englehart K (2011) Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev 48(6):643

    Article  Google Scholar 

  • Scheme E, Englehart K (2013) Training strategies for mitigating the effect of proportional control on classification in pattern recognition based myoelectric control, JPO. J Prosthet Orthot 25:76–83

    Article  Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Network 61:85–117

    Article  Google Scholar 

  • Sengupta S, Basak S, Saikia P, Paul S, Tsalavoutis V, Atiah F, Ravi V, Peters A (2020) A review of deep learning with special emphasis on architectures applications and recent trends. Knowl-Based Syst 194:105596. https://doi.org/10.1016/j.knosys.2020.105596

    Article  Google Scholar 

  • Shrestha A, Mahmood (2019) A review of deep learning algorithms and architectures. In: IEEE Access 7:53040–53065

  • Singh Y N, Singh S K, Ray A K (2012) Bioelectrical signals as emerging biometrics: Issues and challenges, ISRN Signal Process., vol. 2012

  • Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Imag Graph 78:101673

    Article  Google Scholar 

  • Toledo-Pérez D C, Rodríguez-Reséndiz J, Gómez-Loenzo R A (2020) A Study of computing zero crossing methods and an improved proposal for EMG signals. In: IEEE Access, 8:8783–8790

  • Yildirim O, Baloglu UB, Acharya UR (2020) A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput Appl 32:15857–15868. https://doi.org/10.1007/s00521-018-3889-z

    Article  Google Scholar 

  • Zhang B, Zhang S (2017) Pattern-based grasping force estimation from surface electromyography. In: International conference on algorithms, methodology, models and applications in emerging technologies (ICAMMAET)

  • Zhou Z-H, Feng J (2019) Deep forest National Science Review 6(1):74–86. https://doi.org/10.1093/nsr/nwy108

    Article  MathSciNet  Google Scholar 

  • Zhu W, Zeng N, Wang N (2010) Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS® Implementations, NESUG proceedings: health care and life sciences, 19, Baltimore, Maryland p. 67

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bingol University, Bingol, Turkey

    Musab Coskun

  2. Department of Software Engineering, Firat University, Elazig, Turkey

    Ozal Yildirim

  3. Department of Electrical and Electronics Engineering, Firat University, Elazig, Turkey

    Yakup Demir

  4. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  5. Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan

    U. Rajendra Acharya

  6. International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan

    U. Rajendra Acharya

Authors
  1. Musab Coskun
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ozal Yildirim
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yakup Demir
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. U. Rajendra Acharya
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Musab Coskun or U. Rajendra Acharya.

Ethics declarations

Conflict of interest

The authors declare no conflicts 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coskun, M., Yildirim, O., Demir, Y. et al. Efficient deep neural network model for classification of grasp types using sEMG signals. J Ambient Intell Human Comput 13, 4437–4450 (2022). https://doi.org/10.1007/s12652-021-03284-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03284-9

Keywords