Analysis of Different Hand and Finger Grip Patterns using Surface Electromyography and Hand Dynamometry

  • A. Buniya Biomedical Engineering Department/ Al-Khwairzmi College of Engineering/ University of Baghdad/ Iraq
  • Ali H. Al-Timemy Biomedical Engineering Department/ Al-Khwairzmi College of Engineering/ University of Baghdad/ Iraq
  • A. Aldoori Biomedical Engineering Department/ Al-Khwairzmi College of Engineering/ University of Baghdad/ Iraq
  • Rami N. Khushaba Rami Khushaba is within the Faculty of Engineering and Information Technology (FEIT)/ University of Technology/ Sydney (UTS)/ 15 Broadway/ Ultimo 2007/ NSW/ Australia

Abstract

Recording an Electromyogram (EMG) signal is essential for diagnostic procedures like muscle health assessment and motor neurons control. The EMG signals have been used as a source of control for powered prosthetics to support people to accomplish their activities of daily living (ADLs). This work deals with studying different types of hand grips and finding their relationship with EMG activity. Five subjects carried out four functional movements (fine pinch, tripod grip and grip with the middle and thumb finger, as well as the power grip). Hand dynamometer has been used to record the EMG activity from three muscles namely; Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS), and Abductor Pollicis Brevis (ABP) with different levels of Maximum Voluntary Contraction (MVC) (10-100%). In order to analyze the collected EMG and force data, the mean absolute value of each trial is calculated followed by a calculation of the average of the 3 trials for each grip for each subject across the different MVC levels utilized in the study. Then, the mean and the standard deviation (SD) across all participants (3 males and 2 females) are calculated for FCR, FDS and APB muscles with multiple % MVC, i.e 10, 30, 50, 70 % MVC for each gesture. The results showed that APB muscle has the highest mean EMG activity across all grips, followed by FCR muscle. Furthermore, the grip with the thumb and middle fingers is the grip with the highest EMG activity for 10-70% MVC than the power grip. As for the 100% MVC, thumb and middle fingers grip achieved the highest EMG activity for APB muscle, while the power grip has the highest EMG activity for both FCR and FDS muscles.

 

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References

A. H. Ali, “An investigation of Electromyographic (EMG) Control of Dextrous Hand Prostheses for Transradial Amputees,” Ph.D Dessertation, School of Computing and Mathematics Faculty, Plymouth University, 2013.

J. Martin-Martin and A. I. Cuesta-Vargas, “Quantification of functional hand grip using electromyography and inertial sensor-derived accelerations: Clinical implications,” Biomed. Eng. Online, vol. 13, no. 1, p. 161, 2014.

W. M. B. Wan Daud, N. Abas, and M. O. Tokhi, “Effect of Two Adjacent Muscles of Flexor and Extensor on Finger Pinch and Hand Grip Force,” 2018 5th Int. Conf. Control. Decis. Inf. Technol. CoDIT 2018, no. April, pp. 140–145, 2018.

S. N. Sidek and A. J. Haja Mohideen, “Mapping of EMG signal to hand grip force at varying wrist angles,” 2012 IEEE-EMBS Conf. Biomed. Eng. Sci. IECBES 2012, no. December, pp. 648–653, 2012.

G. Hajian, E. Morin, and A. Etemad, “EMG-based Force Estimation using Artificial Neural Networks EMG-based Force Estimation using Artificial Neural Networks,” no. May, 2019.

G. Hajian, E. Morin, and A. Etemad, “PCA-Based Channel Selection in High-Density EMG for Improving Force Estimation,” no. July, pp. 652–655, 2019.

N. H. Gheab and S. N. Saleem, “Comparison Study of Electromyography Using Wavelet and Neural Network ,” Al-Khwarizmi Eng. J., vol. 4, no. 3, pp. 108–119, 2008.

M. K. Sabir and N. K. Muhsin, “An Autocorrelative Approach for EMG Time-Frequency Analysis,” Kecbujournal.Com, vol. 9, no. 1, pp. 39–46, 2013.

K. Li, N. Wei, M. Cheng, X. Hou, and J. Song, “Dynamical coordination of hand intrinsic muscles for precision grip in diabetes mellitus,” Sci. Rep., vol. 8, no. 1, pp. 1–13, 2018.

Published
2020-06-01
How to Cite
Buniya, A., Al-Timemy, A., Aldoori, A., & Khushaba, R. (2020). Analysis of Different Hand and Finger Grip Patterns using Surface Electromyography and Hand Dynamometry. Al-Khwarizmi Engineering Journal, 16(2), 14-23. https://doi.org/10.22153/kej.2020.05.001