This document summarizes a student's project work on investigating work-related musculoskeletal disorders among hand screen printing workers. The student conducted statistical analysis on 385 participants to identify risk factors for lower back pain and upper extremity disorders. Experimental analysis was also conducted on 11 participants to analyze muscle activity and fatigue in the upper extremities and lower back during printing using electromyography. The results found independent risk factors like age, experience and gender were associated with lower back pain and upper extremity disorders. Electromyography analysis found the deltoid, trapezius major and subcapularis muscles experience the highest loads during printing.

1. PROJECT WORK (PHASE – II)
VIVA VOCE
NAME OF THE SCHOLAR NAME OF THE SUPERVISOR
Name : Mohankumar P
Roll No : 13MMR011
Class : M.E. Mechatronics
Semester : IV
Department of Mechatronics
Engineering,
School of Building and Mechanical
Sciences,
Kongu Engineering College,
Erode - 638052
Name : Dr. S.Shankar
Designation : Associate Professor
Department of Mechatronics
Engineering,
School of Building and Mechanical
Sciences,
Kongu Engineering College,
Erode - 638052
Investigations on Work-related Musculoskeletal
Disorders among Hand Screen Printing Workers

2. Introduction
Work-Related Musculoskeletal Disorders(WMSD’s) is one of the
major problems in all intensive labour and manual material
handling tasks.
HSP role first in designing cloth fabrics, with full of manual and
intensive labour and without proper Ergonomic workstation
design.
Current study aimed to investigate the WMSD’s and risk factors
associated with UED’s and LBP.(Statistical)
Also analyze the variation in muscle activity to detect the fatigue
on targeted muscles during the printing work. (Experimental)
sEMG is best method to analyze the muscle activity
experimentally.
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2

3. Literature survey
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Year Title Authors Issues Addressed Approach Conclusion
2014
Muscle fatigue
based evaluation of
bicycle design,
Applied
Ergonomics.
V.
Balasubram
anian, M.
Jaganath, K.
Adalarasu
Investigates the
muscle activity
for various
bicycle design
using sEMG
assessment.
Experimental
approach
Fatigue in right
LDM and ES
significantly
higher in SP
bicycle.
2009
Surface EMG based
muscle activity
analysis for aerobic
cyclist, Journal of
Bodywork and
Movement
therapies.
V.
Balasubram
anian,
Sirinivasan. J
Determines the
muscle activity
of aerobic cyclist
on BBM, TM,
LDM and ES
among LBP group
and control
group
Experimental
approach
Higher fatigue
in LBB among
LBP group than
control group.
3

4. Literature survey
Year Title Authors Issues Addressed Approach Conclusion
2007
Low back pain and
muscle fatigue due
to road cycling- An
sEMG study, Journal
of Bodywork and
Movement
therapies.
V.
Balasubram
anian,
Sirinivasan.
J
Determines the
muscle fatigue
among road
cyclist with and
without Low
back pain.
Experimental
approach
LBP group
showed
significantly
higher pain in
RTM and
erector spinae.
2002
The relationship
between EMG
median frequency
and low frequency
band amplitude
changes at different
levels of muscle
capacity, Clinical
Biomechanics.
G.T. Allision,
T. Fujiwara.
To test the
validity of low
and high
frequency band
amplitude of
sEMG profile as
rep. of Muscle
fatigue
Experimental
approach
Frequency
range varies
based on
muscle
capacity.
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5. Summary of the Literature
 SPSS is a best tool to identify dependency b/w dependent and
independent variables and risk analysis.
 Muscle fatigue can asses efficiently through surface
Electromyography (sEMG) kit.
 Matlab and LabVIEW provide better facility for analyzing the
sEMG signals.
 Root Mean Square value(RMS) and Mean Power Frequency
value(MPF) are used to infer the signals in Time and frequency
domain.
 RMS value decreases with increase in fatigue meanwhile MPF
increases with increase in fatigue.
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5

6. Objective
 Research aimed to investigates the prevalence of work-
related musculoskeletal disorders(WMSD’s) and its risk
factors associated with UED’s and LBP among hand screen
printing workers.
 Analyze Muscle fatigue experimentally on upper extremity
and lower back region during printing process.
Study participants
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Statistical Analysis Experimental Analysis
Participants : 385 Participants : 11
Age [Mean:SD] [35.0±8.11] Age [Mean:SD] [35.8±15.10]
Experience [Mean:SD] [10.0±5.0] Experience [Mean:SD] [12.5±9.0]
Inclusion and Exclusion criteria:
Min 1 yr. experience, No medical
history
Inclusion and Exclusion criteria:
Min 1 yr. experience, No prior
treatment 2days before.

7. Low back disorder and associated risk factors
Risk factor N(Rep. LBP) OR 95% CI
Gender
Male
Female
147
142
1
1.93
1
1.19-3.31
Age
31-40
> 40 years
102
111
1.26
2.89
0.76-2.07
1.63-5.15
Experience
11-15 years
> 15 years
64
109
1.15
1.54
0.64-2.04
0.93-2.56
Height
< 150 cm
> 170 cm
81
21
2.10
0.67
1.14-3.86
0.30-1.48
Smoking
Smokers
Non smokers
50
238
1.05
0.99
0.56-1.94
0.50-1.72
Stress in job
High
Very high
32
4
1.37
1.33
0.60-30.8
0.14-12.031-Oct-18
7

8. UEDs and its associated risk factors
 32
 4
31-Oct-18
Risk factor N OR (NP) OR (UBP) OR (SP) OR (EP) OR (WP)
Gender
Male
Female
211
174
1
1.92
1
1.36
1
1.70
1
1.31
1
1.52
Age
30-40
> 40 years
131
137
1.31
1.18
1.24
1.20
1.20
1.89
1.12
1.36
1.11
1.11
Job tenure
6-15 years
> 15 years
147
101
1.14
1.20
0.95
1.48
1.46
1.83
1.01
1.83
1.34
1.41
Marital status
Single
Married
70
315
1
1.20
1
1.13
1
1.12
1
1.12
1
1.14
Sick leave
Not Availed
Availed
328
57
1
1.89
1
1.79
1
2.13
1
1.72
1
2.94
Stress in job
Moderate
High
282
46
0.99
1.13
0.95
1.48
1.46
1.83
1.01
1.83
1.34
1.41

9. Placement of Electrodes
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Subcapularis
Tres Major
Deltoid
Erector spinae

10. EMG Field Recordings
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10

11. EMG Data Acquisition and analysis
 Muscle activity were recorded continuously for 1 minute using
Biometrics Data LOG sEMG sensors and systems.
 Advanced Integral dry reusable type sEMG sensor with standard 1000
gain, less noise and high input impedance were used to acquire signal
from muscle.
 All the EMG signals were recorded directly at the sampling rate of 1000
Hz and acquired EMG was rectified and processed.
 The acquired data were converted into .wav signal format to analyze the
signal through LabVIEW.
 sEMG signals acquired were analyzed in Time domain and Frequency
domain (power spectrum) by means of Root Mean Square (RMS) and
Mean Power Frequency (MPF).
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12. LabVIEW front panel sEMG signal analysis
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Front panel

13. 31-Oct-18
LabVIEW Block diagram View for signal analysis
12

14. Time and frequency domain Analysis
 Root Mean square
• sEMG behavior during fatigue is associated with decrease in the
root mean square (RMS) of the time signal.
• Weaker muscles are described by positive slopes which indicate
muscle are able to generate less force to maintain the level of
contraction.
 Mean Power Frequency
• Mean power frequency is estimate from the power spectrum.
• Depends on muscle fatigue, Concomitant change in power
spectrum of sEMG signals. The amplitude of higher frequency is
lower than lower frequency due to Fatigue.
• Increase in mean power frequency observes muscle fatigue
action.
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13

15. EMG Recordings of Upper Extremity and Lower back
Muscles during Printing
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S.
No.
Age
(Years)
Experience
(Years)
Deltoid
(mV)
RES
(mV)
LES
(mV)
Tres Major
(mV)
Subcapularis
(mV)
1 20 3 0.161 0.093 0.031 0.079 0.044
2 20 4 0.176 0.107 0.075 0.103 0.046
3 24 5 0.194 0.089 0.080 0.091 0.056
4 26 4.5 0.130 0.021 0.081 0.065 0.058
5 26 11 0.190 0.035 0.031 0.023 0.047
6 32 5 0.151 0.045 0.060 0.054 0.037
7 45 24 0.170 0.042 0.054 0.055 0.089
8 46 25 0.162 0.045 0.052 0.058 0.067
9 50 15 0.180 0.046 0.045 0.206 0.067
10 50 20 0.255 0.030 0.050 0.061 0.081
11 55 21 0.200 0.021 0.053 0.217 0.040

16. Elbow and hip movement(Lower back) Angle
measurement (Goniometer readings)
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Joint
area
Work
Movement
in Angle
(Minimum)
Movement in
Angle
(Maximum)
Elbow Printing 5.5 18.4
Elbow Lifting 2.1 24.9
Lower
Back
Printing 70.6 90.4
Lower
Back
Lifting 40.3 60.6

17. EMG RMS(mv) values with Age

18. EMG RMS(mv) values with Experience

19. Mean and SD error(mV) of Targeted muscles
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20. Conclusions
 Independent risk factors such as age, experience, height with lower than
160 cm, smoking and stressfulness in job were significantly (p<0.05)
associated with the LBP among HSP workers.
 Meanwhile, the factors such as gender, age (30-40), non-smokers, non-
alcohol consumers had significantly (p<0.05) associated with the neck, wrist
pain and elbow pain. Gender, avail sick leave and marital status were
significantly (p<0.05) associated with the shoulder and upper back pain.
 EMG analysis report Deltoid (0.130mV-0.255 mV) shares the major load
then followed by Tres major (0.023 mV-0.217mV) and Subcapularis (0.037
mV-0.089 mV).
 In Erector spinae region, RES shares major load to withstand the posture
(0.037 mV - 0.107 mV) followed by LES (0.031 mV - 0.081 mV). The
experimental results vary accordingly with age, experience and body mass
index (BMI).
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21. Future scope
 Experimental analysis on lower extremity region, and upper neck
region.
 Find the angle movement on upper extremity(neck region), and
lower extremity areas
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22. 31-Oct-18
List of publications
 Shankar S, Naveen Kumar R and Mohankumar P. Job factors, psychosocial stress and
Prevalence of Musculoskeletal disorders among garment related workers of South
India. Slovenian journal of public health. 2014
 Shankar S, Naveen Kumar R and Mohankumar P. Prevalence of work related
musculoskeletal disorders on lower extremity among hand screen printing industry
workers. Work place Health & safety. 2014 (Under Review)
 Shankar S, Naveen Kumar R, Mohankumar P and Karthik J. Prevalence of low back
pain and associated risk factors among fulltime hand screen printing workers. Journal
of Musculoskeletal pain. 2015 (Under Review)
 Shankar S, Naveen Kumar R, Mohankumar P and Karthik J. Work-related physical and
psychosocial risk factors for upper extremity musculoskeletal complaints among Hand
screen printing workers: a cross sectional approach. Human factors and Ergonomics in
manufacturing and service industries. 2015 (Under Review)
 International Conference
o Shankar S, Mohankumar P and Prabu M. Work related musculoskeletal pain and risk
factors variation for male and female workers in hand screen printing industry.
Avidadham’15, Anna University, Chennai. 18 Feb. 2015.
23

23. 31-Oct-18
 Balasubramanian V and Jayaraman S. Surface EMG based muscle activity
analysis for aerobic cyclist. Journal of bodywork and movement therapies.
2009; 13: 34-42.
 Balasubramanian V, Jagannath M and Adalarasu K. Muscle fatigue based
evaluation of bicycle design. Applied ergonomics. 2014; 45: 339-45.
 Srinivasan J and Balasubramanian V. Low back pain and muscle fatigue due to
road cycling—An sEMG study. Journal of Bodywork and Movement Therapies.
2007; 11: 260-6.
 Allison G and Fujiwara T. The relationship between EMG median frequency
and low frequency band amplitude changes at different levels of muscle
capacity. Clinical Biomechanics. 2002; 17: 464-9.
 Van der Hoeven J, Van Weerden T and Zwarts M. Long‐lasting supernormal
conduction velocity after sustained maximal isometric contraction in human
muscle. Muscle & nerve. 1993; 16: 312-20.
 Basmajian JV and De Luca C. Muscles alive. Muscles alive: their functions
revealed by electromyography. 1985; 278: 126.
References
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