Comparison of muscles activity of abled bodied and amputee subjects for around shoulder movement

Abstract

Background:

Worldwide, about 56% of the amputees are upper limb amputees. This research deals a method with two-channel surface electromyogram (SEMG) signal recorded from around shoulder to estimate the changes in muscle activity in non-amputee and the residual limb of trans humeral amputees with different movements of arm.

Objective:

Identification of different muscles activity of near shoulder amputee and non-amputee persons.

Methods:

SEMG signal were acquired during three distinct exercises from three-selected muscles location around shoulder. The participants were asked to move their dominant arm from an assigned position to record their muscles activity recorded with change in position.

Results:

Results shows the muscles activity in scalene is more than the other muscles like pectoralis and infraspinatus with the same shoulder motion. In addition, STFT (Short-Time Fourier Transform) spectrogram with window length of 256 samples at maximum of 512 frequency bins using hamming window has used to identify the signal for the maximum muscles activity with best resolution in spectrum plot.

Conclusions:

The results suggest that one can use this analysis for making a suitable device for around shoulder prosthetic users based on muscles activation of amputee persons.

Keywords

Around shoulder prosthetic STFT spectrogram surface myoelectric signal shoulder movement

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References

Graham

K.Z.

MacKenzie

E.J.

Ephraim

P.L.

Travison

T.G.

and Brookmeyer

, Estimating the prevalence of limb loss in the United States: 2005 to 2050, Archives of Physical Medicine and Rehabilitation 89(3) (2008), 422–429.

http://www.amputee-coalition.org/.

Jeffrey

R.C.

Glenn

S.K.

and Holtz

, Introduction to Surface Electromyography, Aspen Publication, 1998, pp. 1–408.

Gourevitch

and Eggermount

J.J.

, A simple indicator of non-stationary of firing rate in spike trains, Journal of Neuroscience Methods 163 (2007), 181–187.

Soma

Horiuchi

Gonzalez

and Yu

, Classification of upper limb motions from around-shoulder muscle activities, in: IEEE International Conference on Rehabilitation Robotics, Rehab Week Zurich, ETH Zurich, Science City, Switzerland, 2011, pp. 310–315.

Horiuchi , A study on classification of upper limb motions from around-shoulder muscle activities, in: IEEE International Conference on Rehabilitation Robotics, ICORR, Japan, 2009, pp. 311–316.

González

Horiuchi

and Yu

, Classification of upper limb motions from around-shoulder muscle activities: Hand biofeedback, The Open Medical Informatics Journal 4 (2010), 74–80.

Kumar

D.K.

Arjuna

S.P.

and Singh

V.P.

, Towards identification of finger flexions using single channel surface electromyography – Able bodied and amputee subjects, Journal of NeuroEngineering and Rehabilitation 10 (2013), 50.

Camata

T.V.

Dantas

J.L.

Abrão

Brunetto

M.A.O.C.

Moraes

A.C.

and Altimari

L.R.

, Fourier and wavelet spectral analysis of EMG signals in supramaximal constant load dynamic exercise, in: Conference of IEEE, EMBS, Argentina, 2010, pp. 1364–1367.

10.

Ryait

Arora

A.S.

and Agarwal

, Interpretation of wrist operations from SEMG signals at different locations on arm, IEEE Trans. Biomedical Circuits and Systems 4 (2010), 101–111.

11.

Ryait

Arora

A.S.

and Agarwal

, Study of issues in the development of surface EMG controlled human hand, Journal of Materials Science: Materials in Medicine 20(20) (2009), 107–114.

12.

Fey

N.P.

Silverman

A.K.

and Neptune

R.R.

, The influence of increasing steady-state walking speed on muscle activity in below-knee amputees, Journal of Electromyography and Kinesiology 20 (2010), 155–161.

13.

Qian

and Ches

, Joint Time–Frequency Analysis: Methods and Applications, Prentice Hall Professional Technical Reference, 1996.

14.

Alm

J.F.

and Walker

J.S.

, Time–frequency analysis of musical instruments, SIAM Review 44(3) (2002), 457–476.

15.

Lin

and Chen

, Advances in time–frequency analysis of biomedical signals, Critical Reviews in Biomedical Engineering 24 (1996), 1–72.

16.

Papandreou-Suppappol

, Applications in Time–Frequency Signal Processing, Electrical Engineering and Applied Signal Processing Series, CRC Press, 2002.

17.

Mahmoud

S.S.

Hussain

Z.M.

Cosic

and Fang

, Time–frequency analysis of normal and abnormal biological signals, Biomedical Signal Processing and Control 1 (2010), 33–43.

18.

Kilby

and Prasad

, Analysis of surface electromyography signals using discrete Fourier transform sliding window technique, International Journal of Computer Theory and Engineering 5(2) (2013), 321–325.

19.

Raez

M.B.I.

Hussain

M.S.

and Mohd-Yasin

, Techniques of EMG signal analysis: Detection, processing, classification and applications, Bio Proced. Online 8(1) (2006), 11–35.

20.

Englehart

Hudgins

Parker

P.A.

and Stevenson

, Classification of the myoelectric signal using time–frequency based representations, Medical Engineering & Physics 21(24) (1999), 431–438.