Electromyography Signal Analysis Using Time and Frequency Domain for Health Screening System Task

T.N.S. Tengku Zawawi, A.R. Abdullah, W.T. Jin, R. Sudirman, N.M. Saad


Musculoskeletal disorder (MSDs) is
one of the most popular issues of occupational
injuries and disabilities. It has a big impact and
creates a big problem for industries to be resolved.
In MSDs, electromyography (EMG) is one of the
methods to be studied in order to detect MSDs
problem. This research focuses on the EMG signal
analysis by using time domain and frequency
domain (Welch Power Spectral Density) method.
It gives more information from the signal and it
is the most suitable method for classifying the
moments in order to identify the behavioural
of the signals. Axial rotational reach and upper
level reach task from Health Screening Program
(HST) is performed using functional range of
motion (FROM) by considering left and right
biceps brachii muscles to be analysed. There are
two parameters chosen for each time and for each
frequency domain to be tested, which are mean
an absolute value (MAV) and root mean square
(RMS) for time domain. Median frequency (MDF)
and mean frequency (MNF) are for frequency
domain. The results showed that frequency
domain analysis is able to give more parameter
and information of the signal. Upper level reach
acquires more effort to perform the task compared
to axial rotational reach for left and right biceps
brachii. However, different performances of
the signal obtained in classifying the moments
from t-test analysis due to p-value. The best
performance to classify signal characteristics is the
lowest p-value which is 7.369E-05 (MAV), 6.9504E-
05 (RMS), 0.0054 (MDF). However, p-value for
0.0515 is rejected because it is greater than 0.05.
It is concluded that the frequency domain is able
to give more information of the signal, however
for classifications moments, time domain is better
compared to the higher accuracy result. This study
is very important to give the idea in the future
analysis of EMG signal in the aspect of detecting
MSDs in human body in health screening task.

Full Text:



A. Bin et al., “Occupational Disease Among Non-

Governmental Employees in Malaysia: 2002-

,” vol. 3525, no. November 2016, pp. 2002–

, 2013.

“Safety Culture in Malaysian Workplace : An

Analysis of Occupational Accidents,” vol. 5, no.

, pp. 32–43, 2014.

M. S. Murad, L. Farnworth, and L. O. Brien,

“Reliability and validation properties of the

Malaysian language version of the Occupational

Self Assessment version 2 . 2 for injured workers

with musculoskeletal disorders,” vol. 74, no.

May, pp. 226–232, 2011.

S. Executive, “Work-related Musculoskeletal

Disorder (WRMSDs) Statistics, Great Britain

,” pp. 1–20, 2016.

N. Nazmi et al., “A Review of Classification

Techniques of EMG Signals during Isotonic and

Isometric Contractions,” pp. 1–28.

M. Vyhn and P. Kol, “Balance rehabilitation

therapy by tongue electrotactile biofeedback in

patients with degenerative cerebellar disease,”

vol. 31, pp. 429–434, 2012.

M.-ève Chiasson, D. Imbeau, K. Aubry, and A.

Delisle, “Comparing the results of eight methods

used to evaluate risk factors associated with

musculoskeletal disorders,” Int. J. Ind. Ergon.,

vol. 42, no. 5, pp. 478–488, 2012.

A. Phinyomark, P. Phukpattaranont, and C.

Limsakul, “Feature reduction and selection for

EMG signal classification,” Expert Syst. Appl., vol.

, no. 8, pp. 7420–7431, 2012.

N. Q. Z. Abidin, a R. Abdullah, N. Norddin, a

Aman, and K. a Ibrahim, “Leakage Current Analysis

on Polymeric Surface Condition using Time-

Frequency Distribution,” 2012 IEEE Int. Power Eng.

Optim. Conf., no. June, pp. 171–175, 2012.

I. Keshab K. Parhi, Fellow, IEEE, and

Manohar Ayinala, Member, “Spectral Density

Computation,” vol. 61, no. 1, pp. 172–182, 2014.

“67 Feature reduction and selection for EMG

signal classification.pdf.” .

I. Halim, A. R. Omar, A. M. Saman, and I. Othman,

“Assessment of Muscle Fatigue Associated with

Prolonged Standing in the Workplace,” Saf.

Health Work, vol. 3, no. 1, p. 31, 2012.

E. F. Shair, S. A. Ahmad, M. H. Marhaban, S. B.

M. Tamrin, and A. R. Abdullah, “EMG Processing

Based Measures of Fatigue Assessment during

Manual Lifting,” vol. 2017, 2017.

S. Thongpanja, A. Phinyomark, P.

Phukpattaranont, and C. Limsakul, “Mean and

Median Frequency of EMG Signal to Determine

Muscle Force based on Time- dependent Power

Spectrum,” pp. 51–56, 2013.

Lee, C. L., Lu, S. Y., Sung, P. C., & Liao, H. Y.

(2015). Working height and parts bin position

effects on upper limb muscular strain for

repetitive hand transfer. International Journal of

Industrial Ergonomics, 50, 178-185.


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