Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

Feature Extraction and Classification of Surface EMG Signals for Robotic Hand Simulation

Sakshi Sharma, Hemu Farooq, Nidhi Chahal. Published in Signal Processing.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Sakshi Sharma, Hemu Farooq, Nidhi Chahal

Sakshi Sharma, Hemu Farooq and Nidhi Chahal. Article: Feature Extraction and Classification of Surface EMG Signals for Robotic Hand Simulation. Communications on Applied Electronics 4(2):27-31, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Sakshi Sharma and Hemu Farooq and Nidhi Chahal},
	title = {Article: Feature Extraction and Classification of Surface EMG Signals for Robotic Hand Simulation},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {2},
	pages = {27-31},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Feature Extraction and Classification of Surface Electromyography (EMG) signals provide an access for the development of Robotic Hand. EMG signals stands for electromyography signals. These are called the bio signals. Bio signal means a collective electrical signal acquired from any organ that represents a physical variable of interest. The EMG signal being a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. [1]. The nervous system always controls the muscle activity (contraction/relaxation). Hence, the EMG signal is a complicated signal, which is controlled by the nervous system and is dependent on the anatomical and physiological properties of muscles.

In this paper, we have discussed different steps in analyzing the EMG signals. The first step is to analyze the surface EMG signal from the subject’s forearm using Discrete Wavelet Transform and extract features using the singular value decomposition.

The second step is to call the different feature values into linguistic terms by using Fuzzy Logic Classifiers in order to recognize different degrees of freedom like open to close, close to open etc.

This paper will give in depth insight in the field of EMG signal and has provided more efficient work when compared to conventional works and efficiency is 99%.


  1. Praveen Lakkur Srinivasa, Nagananda S. R,”Development of two Degree of freedom (DOF) Bionic Hand for Below Elbow Amputee,” Proceedings of the IEEE international conference on Electronics, Computing and Communication Technologies, Bangalore, 17-19 Jan. 2013, pp.1-6
  2. Shaikh Anowarul Fattah, A. B. M. Sayeed Ud Doulah, Md. Asif Iqbal and Celia Shahnaz, Wei-Ping Zhuand M. Omair Ahmad, “Identification of Motor Neuron Disease Using Wavelet Domain Features Extracted from EMG Signal,” Proceedings of the IEEE International Symposium on Circuits and systems, Beijing, 19-23 May 2013, pp.1308-1311
  3. Chetas D.Joshi, Uttama Lahiri and Nitish V. Thakor, “Classification of Gait Phases from Lower Limb EMG Application to Exoskeleton Orthosis,” IEEE Transactions on point-of-Care Healthcare Technologies, Banglore, 16-18 Jan 2013, pp.228-231
  4. Zhiqiang Zhang, Charence Wong, Guang-Zhong Yang, “Foorearm Functional movement Recognition Using Spare Channel Surface Electromyography,” Proceedings of the IEEE International Conference on Body Sensor Networks, 6-9 May 2013, pp.1-6
  5. Kexin Xing, Qi Xu, YeguiLin, “Identification Scheme of Surface Electromyography of Upper Limb Movement,” Journal of Networks, Vol. 8, No. 4, April 2013
  6. Francesco Tenore, Ander Ramos,Amir Fahmy, Soumyadipta Acharya, Ralph Etienne-Cummings and Nitish V. Thakor, “Towards the control of individual Fingers of a Prosthetic Hand Using Surface EMG Signals,” Proceeding of International Conference on Engineering in Medicine and Biology Society, Lyon August 23-26, 2007, pp.6145-6148
  7. Md. Rezwanul Ahsan, Muhammad Ibn Ibrahimy, Othman O. Khalifa, “Electromyography (EMG) Signal based Hand Gesture Recognition using Artificial Neural Network (ANN),” Proceedings of International Conference on Mechatronics (ICOM), Kaula Lumpur, Malaysia 17-19 May 2011, pp.1-6
  8. Han Liu, Yun-Wei Huang and DingLiu, “Multi-class Surface EMG classification using Support Vector Machines and Wavelet Transform,” Proceedingsof the 8th World Congress on Intelligent Control and Automation, Jinan, July 6-9 2010, pp.2936-2967
  9. Eldin Henry Shroffe D, P. Manimegalai, “Hand Gesture Recognition Based on EMG Signals Using ANN,” International Journal of Computer Application Issue 3, Vol. 2, April 2013
  10. Navleen Singh Rekhi, Ajat Shatru Arora, Sukhwinder Singh , Dilbag Singh, “Multi-Class SVM Classification of Surface EMG Signal for Upper Limb Function,” Proceedings of
  11. ICBBE 3rd International Conference on Bioinformatics Biomedical Engineering, Beijing, 11-13 June, 2009, pp.1-4
  12. Farideh Ebrahim, Mohammad Mikaeili, Edson Estrada, Homer Nazeran, “Automatic Sleep Classification Based on EEG Signals by using Neural Networks and Wavelet Packet Coefficients,” Proceedings of 30th Annual IEEE International Conference, August 20-24, 2008, pp.1151-1154
  13. A.Phinyomark, C. Limsakul, P. Phukpattaranont, “Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification,” Measurement Science Review, Vol. 11, No. 2, 2011
  14. Zhiguo Yan, Zekun Liu, “The Study on Feature Selection Strategy in EMG Signal Recognition,” Proceedings of 2013 ICME International Conference on Complex Medical Engineering, May 25-28, pp. 711-716
  15. Rajesh Kumar Tripathy, Ashutosh Acharya, Sumit Kumar Choudhary, Santosh Kumar Sahoo, “Artificial Neural Network based Body Posture Classification from EMG Signal Analysis”, Indonesiann Journal of Electrical Engineering and Informatics (IJEEJ), Vol. 1, No. 2 June 2013, pp. 59-63


EMG signal, DWT, fuzzy classifier, feature extraction