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21-31 

21 

 

 

 

Article 

Analysis of fatigue of construction workers based 

on electromyographic signals 
Xiaohong Gui*, Xinuo Fang, Mengting Li, Mingjun Dai, Lianbin Su 

China University of Mining and Technology, Beijing, Beijing 100083, China 

               A R T I C L E   I N F O 
 

Article history: 
Received 25 May 2025  
Received in revised form 
01 July 2025 
Accepted 14 July 2025 
 
Keywords:  
Electromyographic signals, Construction workers, 
Time-frequency analysis, Fatigue monitoring 
 
*Corresponding author 
Email address: 
gxhbox@sina.com 
 
  
DOI: 10.55670/fpll.fusus.3.4.3 
 

A B S T R A C T 
 

Aiming to address the fatigue issue of construction workers resulting from high-

intensity physical labor, this paper proposes a fatigue analysis method based on 

surface electromyographic signals (sEMG), focusing on the handling operation 

as the research object, to explore the fatigue characteristics of construction 

workers' muscles and significant monitoring indices. By collecting sEMG signals 

under different fatigue levels, we analyze the trends of time-frequency domain 

indicators (root mean square value RMS, integral EMG value IEMG, median 

frequency MF, mean power frequency MPF, and over-zero rate ZCR). The 

experimental results show that with the increase of fatigue, the RMS and IEMG 

of brachioradialis and erector spinae increase significantly, while the MF and 

MPF decrease significantly, which reflects the physiological mechanism of the 

decrease of muscle contraction efficiency and the enhancement of neural drive. 

The changes in the indexes of erector spinae are more significant than those of 

brachioradialis due to the higher stability load and the activation characteristics 

of fast muscle fibers. Through the test of intergroup variability, RMS, IEMG, MF, 

and MPF are selected as the core indicators for fatigue monitoring. This study 

provides an objective, quantitative basis for labor protection in the construction 

industry and lays a theoretical foundation for the real-time monitoring of 

occupational fatigue and the optimization of work efficiency. 

 

1. Introduction 

As a vital pillar of the national economy, the construction 

industry plays a crucial role in driving high-quality economic 

development [1]. However, due to the high labor intensity and 

poor working environment faced by construction workers, 

the safety accident rate in the construction industry has long 

been higher than that of other industries [2]. Construction 

workers are often required to perform physically demanding 

tasks in awkward working postures for extended periods, 

making them prone to occupational fatigue [3]. This condition 

can lead to the occurrence of unsafe behaviors that increase 

the risk of safety accidents and other occupational health 

problems [4]. Fatigue has been recognized as one of the main 

causes of safety accidents in the construction industry, so 

research on fatigue in construction workers is very 

important. Most of the fatigue produced by construction 

workers is physiological fatigue, and the reason for this 

fatigue is due to the lack of metabolic capacity of the body 

caused by prolonged labor or strenuous exercise, resulting in 

a significant accumulation of lactic acid and carbon dioxide 

and other metabolites in the muscle, this local acidic 

environment will interfere with the process of calcium ions 

and calponin binding in the myocyte, which will inhibit the 

normal contraction of the muscle, and ultimately lead to the 

physiological fatigue state of the organism [5]. At present, 

numerous scholars have conducted research on human 

fatigue. Bai Wei et al. [6] proposed a method of judging driver 

fatigue by testing surface electromyographic signals through 

electromyography experiments for the problem of muscle 

fatigue and injury that can be easily caused by the process of 

drivers getting into the car. Yang Yanpu et al. [7] Effective 

identification of upper limb muscle fatigue state in hand-over-

head operation based on support vector machine by 

collecting surface EMG signals of the subjects as well as 

subjective fatigue state. Xin Yunsheng et al. [8] analyzed the 

muscle fatigue of monorail crane drivers by conducting 

electromyographic testing studies on 16 muscles prone to 

fatigue. Liang Zhanhun et al. [9] analyzed and researched the 

local muscle fatigue of climbing workers through surface EMG 

signals, explored the characteristic pattern of change of 

Future Sustainability 

Open Access Journal 

https://doi.org/10.55670/fpll.fusus.3.4.3 

 

 

 

 

 

 

 

 

November 2025| Volume 03 | Issue 04 | Pages 21-31 

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X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

22 

 

surface EMG signals and its relationship with the subjective 

fatigue evaluation value in the process of operation, and 

provided data support for objectively evaluating the local 

muscle fatigue of climbing operation and preventing work-

related musculoskeletal disorders. Xu Zhao et al. [10] 

introduced sEMG signal recognition and motion capture 

technology into the process of fatigue state monitoring, 

proposing a fatigue analysis method that integrates an 

improved EMG fatigue threshold algorithm and 

biomechanical analysis. Wang Hongpeng et al. [11] selected a 

typical road section of the Pamir Plateau in Xinjiang to 

conduct a real driving test to investigate the fatigue 

characteristics of the neck muscles of plateau highway drivers 

under the effect of continuous driving time and altitude by 

means of surface electromyographic signals. Antwi-Afari et al. 

[12] analyzed the loading conditions of different body parts of 

the workers in carrying out a study related to manual material 

handling operations, which showed that Reasonable control 

of lifting weight, improvement of working posture, and 

reduction of repetitive operations can effectively reduce the 

probability of fatigue. Wang et al. [13] conducted a study on 

roofing operations and found that different working postures 

and frequency of operations can cause significant changes in 

the electromyographic activity level of the lower back 

muscles of the workers, which suggests that the effect of the 

mode of operation on the muscular fatigue should not be 

ignored. Shariatzadeh et al. [14] designed and manufactured 

a novel wearable sensor system with sEMG electrodes and 

motion tracking sensors for monitoring dynamic muscle 

movements in the human body. 

Currently, there are few studies on the analysis of 

operational fatigue of construction workers, and few studies 

for the analysis of physiological fatigue of construction 

workers. Therefore, this paper analyzes the operation fatigue 

of construction workers based on EMG signals and combined 

with subjective fatigue perception, to explore the intrinsic 

connection between fatigue perception and EMG signals, to 

reveal the dynamic change law of the fatigue state of 

construction workers, so as to improve the efficiency of 

construction workers, to reduce the unsafe behaviors 

triggered by physical fatigue, and to reduce the probability of 

safety accidents. 

2.  Experimental design and method 

2.1 Subjects 

 Since construction workers are predominantly male and 

generally have good physical fitness, 12 healthy adult male 

construction workers were selected as subjects in this study. 

All subjects have high physical fitness and typically exercise 

for more than 1 hour per day. The specific physical data are 

as follows: age 22-26 years old, height 170-182cm, weight 65-

80kg, BMI 22.02-24.17. Before the experiment, to ensure that 

all the subjects can experiment with the best physical state, 

the subjects are required to maintain sufficient sleep, prohibit 

alcohol, coffee, sports drinks and other beverages, and the 

experimental time is selected in the morning or the afternoon, 

to maximize the experimental time was chosen in the 

morning or afternoon to maximize the simulation of the 

construction time in the building construction site. 

 

2.2  Research methods 

 Adopting the research method of laboratory simulation, 

the above research objects were selected to simulate the work 

of construction workers in the laboratory. The manual 

handling operation was chosen as the experimental task, 

which has lower requirements for the laboratory 

environment and can be carried out in a controlled setting. 

The Noraxon Ultium EMG wireless surface EMG instrument 

was used to monitor EMG signals during the experiments, and 

the Borg scale was used to quantify the physical state 

objectively. 

2.3  Electrode arrangement of EMG equipment 

 EMG signals are measured by electrodes placed on the 

surface of the skin, which are capable of recording the signals 

of electrical activity of the muscles in response to nerve 

stimulation, thus reflecting the physiological state and 

functional changes of the muscles. Based on the results of 

related research [15-17], the brachioradialis and erector 

spinae muscles are selected as the target muscle groups in 

this paper. The electrode arrangement position of EMG 

signals is shown in Figure 1, and only one side of the muscle 

can be measured in the measurement, so the right half of the 

muscle of the subject is chosen to be measured. 

 

Figure 1. Myoelectric electrode arrangement (① is the 

brachioradialis muscle, and ② is the erector spinae muscle) 

 

 

2.4 Experimental procedure 

 Before the beginning of the experiment, wear the 

physiological monitoring equipment for the subject correctly 

and make sure that the physiological monitoring equipment 

can work normally. Then, the subjects entered the resting 

session and kept sitting still for 10 minutes in order to bring 

their physiological status to the basal level. At the end of the 

resting period, the experiment began with subjects lifting a 25 

kg weight from point "A" and stacking five weights to point 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

23 

 

"B" according to the diagonal path shown in Figure 2. After 

completing each set of tasks, the subjective fatigue perception 

according to the Borg scale was filled in, and then the weights 

were carried from point "B" to point "A" one by one. This 

process was repeated until the subject felt completely 

exhausted and signaled the termination of the experiment. 

Physiological indexes of the subjects were recorded 

throughout the experiment. 

 
Figure 2.  Manual handling simulation operation 

2.5 Data collection and recording 

2.5.1 Subjective fatigue scale 

  The Borg Perceived Exercise Intensity Scale (RPE) was 
used in the experiment, which ranges from 0-10, indicating 
the subject's perception of fatigue in accomplishing a specific 
task, from "no feeling" to "maximum effort". The Borg scale 
score was recorded once after each subject completed five 
lifting tasks until the fatigue level reached its limit. In order to 
systematically analyze the fatigue of construction workers 
and reflect the subjective fatigue state of the subjects in a 
more detailed way, the Borg scale scores of the subjects in the 
process of lifting and carrying tasks were divided into three 
fatigue levels: 0-3 points were divided into the low-fatigue 
group, 4-6 points were divided into the moderate-fatigue 
group, and 7-10 points were divided into the severe-fatigue 
group. 

2.5.2  Pre-processing of EMG signals 

The EMG signal will be interfered by the measurement 
environment noise, inherent noise of the equipment and 
baseline drift in the process of acquisition and recording, so 
to accurately extract the surface EMG signal that can reflect 
the fatigue state of the body, it is necessary to use the 
corresponding filtering method to preprocess the original 
EMG signal. 
 (1) Inherent noise of the acquisition instrument: In the 

process of EMG signal acquisition, the inherent noise of the 

measuring instrument is one of the main sources affecting the 

signal quality. In order to minimize the noise interference in 

signal acquisition, the skin surface where the target muscle 

group is located is cleaned as necessary before the 

experiment, and the distance between the electrodes on the 

paired surfaces is reasonably controlled. 

 (2) Measurement of environmental noise: Environmental 

noise refers to the noise pollution introduced by the 

peripheral electrical equipment during the signal acquisition 

process, of which industrial frequency interference is the 

most important form of interference. According to China's 

power system standards, the operating frequency of the AC 

power supply network is set to 50Hz, which causes the 

spectral energy of industrial frequency interference to show 

significant aggregation characteristics near the 50Hz 

frequency point. Therefore, this paper adopts a Butterworth-

type bandpass filter to suppress the industrial frequency 

interference, and the filter parameters are set to a 49- 51Hz 

passband range. 

 (3) Time-frequency domain feature index extraction: In 

order to comprehensively evaluate the change rule of EMG 

signals under fatigue state, this paper calculates the root 

mean square (RMS) and integral electromyography (IEMG) of 

the filtered EMG signals of the brachioradialis and erector 

spinae through the average value of the sliding window of 500 

milliseconds. Meanwhile, the median frequency (MF), mean 

power frequency (MPF), and zero-crossing rate (ZCR) were 

derived after calculating the power spectral density using the 

ScipyWelch function. In addition, the time-domain features of 

EMG, RMS, and IEMG were normalized by the corresponding 

values of maximum muscle force (MVC) of the respective 

muscles. These feature indicators reflect the degree of muscle 

fatigue from multiple dimensions, such as signal intensity, 

total activity, and frequency distribution, respectively, and 

have good sensitivity and stability in muscle fatigue detection. 

The specific calculation process of each feature index is as 

follows: 

RMS: It is a kind of feature indicator reflecting the overall 

amplitude of the signal, mainly used to assess the intensity of 

muscle activity, commonly used in fatigue monitoring and 

action recognition. The calculation formula is shown in 

Equation (1). 

𝑅𝑀𝑆 = √1

𝑁
∑

𝑖=1
𝑁

𝑥𝑖
2                                                  (1) 

Where xi is the signal value of the ith sampling point; N is the 

total number of sampling points. 

IEMG: It is a feature that sums the absolute values of the 

signals and is used to assess the overall output of the muscle 

activity, which is suitable for the analysis of muscle fatigue 

over a long period of time. The calculation formula is shown 

in Equation (2). 

𝐼𝐸𝑀𝐺 = ∑

𝑖=1
𝑁

|𝑥𝑖|                                             (2) 

MF: is the frequency in the power spectrum that divides the 

total power into two equal parts, commonly used in fatigue 

monitoring. MF usually drifts to lower frequencies as the 

muscle fatigues. The formula is shown in Equation (3). 

∫0
𝑀𝐹
𝑃(𝑓)𝑑𝑓 = ∫𝑀𝐹

𝑓𝑚𝑎𝑥
𝑃(𝑓)𝑑𝑓               (3)   

Where P(f) is the power spectral density of the signal, fmax is 

the maximum frequency. 

 MPF: is the weighted average of the frequency distribution of 

the signal, similar to MF, used to assess muscle fatigue. The 

calculation formula is shown in Equation (4). 

 𝑀𝑃𝐹 =
∫0
𝑓𝑚𝑎𝑥𝑓⋅𝑃(𝑓)𝑑𝑓

∫0
𝑓𝑚𝑎𝑥𝑃(𝑓)𝑑𝑓

                                                                            (4) 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

24 

 

 ZCR: is the number of times the signal passes through the 

zero point within a certain time window and is used to 

analyze the frequency changes of EMG signals, especially the 

frequency dynamics during rapid muscle contraction and 

relaxation. The calculation formula is shown in Equation (5). 

𝑍𝑅𝐶 = ∑

𝑖=1
𝑁−1

1((𝑥𝑖 ⋅ 𝑥𝑖+1) < 0)                                                         (5) 

3.  Results and discussion 

3.1  Subjective fatigue 

 According to the statistical results, there were 57 low 

fatigue groups, 262 moderate fatigue groups, and 141 severe 

fatigue groups, and the difference between the three groups 

was statistically significant (P< 0.05). With the progress of the 

handling task, the subjective fatigue perception of the 

subjects gradually deepened, and the Borg score showed a 

significant upward trend. The mean value of the scores in the 

low fatigue group remained at a low level, approximately in 

the range of 1-3 points, reflecting that the subjects' fatigue 

perception was relatively light at this fatigue level; whereas 

the scores in the moderate fatigue group were in the range of 

4-6 points, showing an increase in fatigue perception; the 

mean value of the scores in the severe fatigue group was 

significantly higher than that of the previous two groups, 

usually in the range of more than 7 points, showing that the 

fatigue perception of the subjects reached the peak in the 

state of severe fatigue, as shown in Figure 3. 

Figure 3.   Mean values of scores of different subjects at three levels 

of fatigue 

3.2 EMG signal analysis 

3.2.1 Descriptive analysis of EMG signal characteristics 

indicators 

Based on the three different fatigue groupings, the 
mean values of the characteristic indexes of the subjects 
under different fatigue levels were calculated. The changes of 
the mean values of the physiological indexes of the 12 
subjects from low fatigue to severe fatigue states are shown 
in Figure 4 (a, b, c, d, e). From Figure 4 (a) to (e), it can be seen 
that as the fatigue level rises, the time-frequency domain 
indexes of EMG signals of brachioradialis and erector spinae, 
such as RMS and IEMG indexes, show an upward trend; such 
as the MF and MPF indexes, show a downward trend; and the 

trend of change of the ZCR indexes is not obvious, and the 
trend of change of each subject is not consistent. Specifically: 
 (1) RMS of brachioradialis muscle increased from low fatigue 

(0.05± 0.30) to severe fatigue (0.08± 0.04), and IEMG low 

fatigue (0.06± 0.07) to severe fatigue (0.09± 0.10). 

 (2) The RMS of the erector spinae muscle increased from low 

fatigue (0.46± 0.67) to severe fatigue (1.05± 1.73), and IEMG 

from low fatigue (0.10± 0.08) to severe fatigue (0.22± 0.26). 

 (3) MF of the brachioradialis muscle decreased from low 

fatigue (0.13± 0.11) to severe fatigue (0.11± 0.09), and MPF 

decreased from low fatigue (0.15± 0.12) to severe fatigue 

(0.13± 0.11). 

 (4) MF decreased from low fatigue (0.93± 0.31) to severe 

fatigue (0.74± 0.22), and MPF decreased from low fatigue 

(0.89± 0.27) to severe fatigue (0.72± 0.18) in the erector 

spinae muscle. 

 (5) The ZCR of the brachioradialis and erector spinae muscles 

showed a small increase in their mean values, although the 

trend was not significant. ZCR increased from low fatigue 

(0.10± 0.02) to severe fatigue (0.11± 0.03) for brachioradialis, 

and from low fatigue (0.10± 0.02) to severe fatigue (0.11± 

0.03) for erector spinae. 

 As above, the trend of RMS vs. IEMG suggests that as the 

lifting operation progresses, the subjects' muscle fatigue 

builds up, resulting in less efficient muscle contraction and 

higher neural drive is required to maintain force output.The 

trend of MF vs. MPF suggests that as the muscle fatigue builds 

up, the muscle's fast muscle fibers (Type II) take the lead in 

fatigue due to lactic acid buildup and energy depletion, and 

the slow muscle fibers (Type I) dominate the contraction, 

resulting in a slowing of action potential conduction and an 

increase in the proportion of low-frequency components.  

The trend of ZCR shows that fatigue results in a widening 

of the action potential waveform, a decrease in high-

frequency oscillations, and a decrease in the number of times 

the signal crosses the baseline (zero point) per unit time.  

Among them, by comparing the change amplitude of the 

characteristic indexes in the time-frequency domain of the 

erector spinae and brachioradialis, it can be found that the 

change amplitude of the characteristic indexes of the erector 

spinae is significantly larger than that of the brachioradialis. 

The mechanism can be summarized as the following three 

points [18,19]: 

 (1) Biomechanical load differences: the erector spinae 

muscle is continuously subjected to spinal compression and 

shear forces during handling operations, and the muscle 

fibers (especially type II fast muscle fibers) are more prone to 

fatigue; 

 (2) Distribution of muscle fiber types: the vertical spine 

muscle has a higher proportion of fast muscle fibers (about 

60%), which enter anaerobic metabolism earlier under 

sustained loading, leading to lactic acid accumulation and left 

shift of the frequency spectrum; 

 (3) Recruitment pattern of motor units: the erector spinae 

muscle needs to maintain postural stability, and the motor 

units showed high-threshold synchronized activation, with 

greater central nervous system drive intensity. 

 

 

 

 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

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a) RMS change trend chart for test subjects 

b) IEMG change trend chart for test subjects 

c) MF change trend chart for test subjects 

d) MPF change trend chart for test subjects 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

26 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

In summary, although some subjects, due to individual 
differences, have outstanding anti-fatigue ability of the 
muscle groups of their forearms or erector spinae, and the 
trend of the time-frequency domain characteristic indexes is 
not obvious, the overall trend is the same as the findings 
obtained by the previous research [20,21], which confirms 
that the standard EMG acquisition equipment used in this 
paper can effectively capture muscle fatigue under dynamic 
operating environments, and further verifies the reliability of 
the experimental data. 

3.2.2 Screening of fatigue significant indicators of EMG 

signals 

 By statistically analyzing the characteristic indicators 
of the EMG signals of the brachioradialis and erector spinae 
muscles, the significant indicators under different fatigue 
levels were screened. Since the data in this paper belongs to 
the relevant samples, the normality test was firstly performed 
on the above characteristic indexes. For the data that satisfy 
the normality test, single-group repeated-measures ANOVA 
and Bonferroni post-hoc two-by-two comparisons were 
carried out. For the data that do not satisfy the normality test, 
Friedman's test and Wilcoxon signed-rank test were carried 
out to carry out post-hoc two-by-two comparisons. A 
normality test was done for the dependent variable as shown 
in Table 1, and due to the small sample size, the Shapiro-Wilk 
test results were chosen to be used. From the results of the 
Shapiro-Wilk normal distribution test, it can be seen that the 
P value of RMS and ZCR indexes of brachioradialis muscle and 
MF, MPF, and ZCR indexes of erector spinae muscle in 
different fatigue groups were all greater than 0.05, obeying 
normal distribution; the rest of the indexes did not obey 
normal distribution.  
A single-group repeated-measures ANOVA was performed on 
the above indexes that obeyed normal distribution, and 
Mauchly's test of sphericity was performed on the dependent 
variable, and the results are shown in Table 2. Mauchly's test 
of sphericity showed that the data of RMS and ZCR indexes of 
the brachioradialis muscle, and the data of MF and MPF 
indexes of the erector spinae muscle, did not satisfy the  

 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

assumption of sphericity (P1=0.000＜0.05, P2=0.001＜ 0. 05, 
P(3)=0.001< 0. 05, and P(4)=0.001<0. 05, and P(4)=0.001< 0. 
05, P3=0.012＜ 0.05, P4=0.021＜ 0.05), when the dependent 

variable violates the conditions of the spherical assumption 
needs to be epsilon (ε) correction, as can be seen in Table 2, 
the Huynh-Feldt method was chosen to be more effective in 
the correction, and after the correction of ε1=0.553, ε2=0.559, 
ε3=0.672, ε(4)= 0.702. The data of the ZCR index for the 
erector spinae muscle satisfied the spherical assumption 
(W=0.654, P=0.120＞ 0.05), so the results under the spherical 
assumption were read directly.  

The results of the within-subjects effect test for each 
level of the dependent variable are shown in Table 3, and 
combined with the results of the analysis above, it can be seen 
that by the Shapiro-Wilk test, the dependent variables in each 
group of the RMS and ZCR indexes for the brachioradialis 
muscle and the MF, MPF, and ZCR indexes for the erector 
spinae muscle obeyed a normal distribution (P＞0.05). The 
dependent variables of RMS (brachioradialis), ZCR 
(brachioradialis), MF (erector spinae), and MPF (erector 
spinae) indexes did not satisfy the assumption of sphericity 
by Mauchly's sphericity hypothesis test, and ε1= 0.553, ε2= 
0.599, ε3= 0.672, and ε4= 0.702 after correction by Huynh-
Feldt's method. After correction, F(1)(1.105,12.160) = 
12.958, P1= 0.003＜ 0.05, i.e., the difference is statistically 

significant; F2(1.197,13.171) = 0.736, P2= 0.430＞ 0.05, i.e., 
the difference is not statistically significant; F3(1.344,14.787) 
= 9.843, P3= 0.004＜ 0.05, i.e., the difference is statistically 

significant; F4(1.403,15.437) = 11.738, P4= 0.002＜ 0.05, i.e., 
the difference is statistically significant.  

The dependent variable of ZCR (erector spinae muscle) 
index satisfies the assumption of sphericity, and under the 
degree of sphericity, F (2,22) = 4.082, P=0.031＜ 0.05, i.e., the 
difference is statistically significant. The results of the 
Bonferroni correction for the above statistically significant 
indicators are shown in Table 4.  

 
 
 
 

 

 
 

Figure 4. Changes in the mean values of physiological indicators under different fatigue states of the subjects (left figures: Brachioradialis 

muscle, right figures: Erector spinae muscle) 

 

e) ZCR change trend chart for test subjects 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

27 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

As can be seen from Table 4, RMS (brachioradialis 
muscle), MF (erector spinae muscle), and MPF (erector spinae 
muscle) indices were found to be statistically significant in the 
low fatigue group versus the moderate fatigue group 
(P1=0.012＜0.05, P2=0.035＜0.05, and P3=0.017＜0.05), 
and in the low fatigue group versus the severe fatigue group 
(P1=0.011＜0.05, P2=0.018＜0.05, P3=0.011＜0.05), 
moderate fatigue group versus severe fatigue group 
(P1=0.022＜0.05, P2=0.032＜0.05, P3=0.015＜0.05) differed 

significantly, indicating that RMS (brachioradialis muscle), 
MF (erector spinae muscle), and MPF (vertical spine muscle) 
indexes were significantly different between different fatigue 
groups, and the effect on fatigue ZCR (erector spinae) indexes 
no significant difference between different fatigue groups  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

(P＞0.05), which means that they were not significant for 
fatigue.  

Friedman's nonparametric test was performed on the 
indicators of IEMG (brachioradialis), MF (brachioradialis), 
MPF (brachioradialis), RMS (erector spinae), and IEMG 
(erector spinae) that did not obey normal distribution, and 
the results of the test are shown in Table 5. The P-value of a 
certain indicator in Friedman's test is less than 0.05, 
indicating that at least one group of the indicators between 
different fatigue grades has a significant difference. 

 As can be seen from the table, IEMG (P=0.000＜0.05), 

MF (P=0.000＜0.05), MPF (P=0.000＜0.05) for 

brachioradialis muscle, and RMS (P=0.000＜0.05), IEMG 

(P=0.000＜0.05) for erector spinae muscle indicated that at 

Table 1. Normality test of characteristic indexes of EMG signals 

 Characteristic 
index 

 Fatigue level  Kolmogorov-Smirnov a  Shapiro-Wilk 

 Statistic  df  Sig.  statistic  df  Sig. 
 RMS 

 (brachioradialis) 
 Low fatigue  .123  12  .200  .960  12  .785 

 Moderate fatigue  .106  12  .200  .986  12  .998 

  Severe fatigue  .102  12  .200  .986  12  .998 

 IEMG 
 (brachioradialis) 

 Low fatigue  .296  12  .005  .717  12  .001 
 Moderate fatigue  .329  12  .001  .660  12  .000 

 Severe fatigue  .301  12  .004  .696  12  .001 
 MF 

 (brachioradialis) 
 Low fatigue  .287  12  .007  .740  12  .002 

 Moderate fatigue  .305  12  .003  .704  12  .001 
 Severe fatigue  .343  12  .000  .712  12  .001 

 MPF 
 (brachioradialis) 

 Low fatigue  .268  12  .017  .763  12  .004 
 Moderate fatigue  .285  12  .008  .730  12  .002 

 Severe fatigue  .326  12  .001  .736  12  .002 
 ZCR 

 (brachioradialis) 
 Low fatigue  .170  12  .200  .948  12  .606 

 Moderate fatigue  .174  12  .200  .908  12  .199 
 Severe fatigue  .211  12  .145  .890  12  .119 

 RMS 
 (erector spinae) 

 Low fatigue  .378  12  .000  .539  12  .000 
 Moderate fatigue  .384  12  .000  .525  12  .000 

 Severe fatigue  .433  12  .000  .558  12  .000 
 IEMG 

 (erector spinae) 
 Low fatigue  .273  12  .014  .752  12  .003 

 Moderate fatigue  .311  12  .002  .654  12  .000 
 Severe fatigue  .367  12  .000  .628  12  .000 

 MF 
 (erector spinae) 

 Low fatigue  .170  12  .200  .963  12  .825 
 Moderate fatigue  .142  12  .200  .942  12  .521 

 Severe fatigue  .110  12  .200  .972  12  .934 
 MPF 

 (erector spinae) 
 Low fatigue  .157  12  .200  .937  12  .458 

 Moderate fatigue  .203  12  .187  .955  12  .708 
 Severe fatigue  .104  12  .200  .986  12  .998 

 ZCR 
 (erector spinae) 

 Low fatigue  .127  12  .200  .961  12  .802 
 Moderate fatigue  .152  12  .200  .937  12  .456 

 Severe fatigue  .139  12  .200  .930  12  .377 
 

 Table 2. Mauchly's test of sphericity for each indicator 

 Within-
subjects 

effect 

 Indicator  Mauchly's W  Approximate 
chi-square 

 df  Sig.  Epsilon 

 Greenhouse-
Geisser 

 Huynh-Feldt  Lower 
limit 

 Fatigue  RMS 
 (brachioradialis) 

 .149  19.040  2  .000  .540  .553  .500 

 ZCR 
 (brachioradialis) 

 .260  13.465  2  .001  .575  .599  .500 

 MF 
 (Vertebrae) 

 .410  8.912  2  .012  .629  .672  .500 

 MPF 
 (Vertebrae) 

 .463  7.708  2  .021  .650  .702  .500 

 ZCR 
 (Vertebrae) 

 .654  4.245  2  .120  .743  .832  .500 

 

 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

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least one group had significant differences between different 
fatigue levels. To further screen the indicators that were 
significant for fatigue among different fatigue groups, the 
Wilcoxon signed rank test was performed to compare the 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

above indicators two by two, as shown in Table 6. All 
indicators in the table have significant differences between 
different fatigue groups (P＜0.05), indicating that the 
indicators in the table are significant for fatigue. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

Table 3. Within-subjects effect test for each indicator 

 Indicator   Type III sum 

of squares 

 df  mean square  F  Sig.  η² 

 RMS 

 (brachioradialis) 

 Sphericity-

assumed 

 .004  2  Sphericity-

assumed .004 

2 

 12.958  .000  .376 

 Greenhouse-

Geisser 

 .004  1.080  .004  12.958  .003  .376 

 Huynh-Feldt  .004  1.105  .004  12.958  .003  .376 

 Lower limit  .004  1.000  .004  12.958  .004  .376 

 ZCR 

 (brachioradialis) 

 Sphericity-

assumed 

 .000  2  .000  .736  .490  .033 

 Greenhouse-

Geisser 

 .000  1.150  .000  .736  .425  .033 

 Huynh-Feldt  .000  1.197  .000  .736  .430  .033 

 Lower limit  .000  1.000  .000  .736  .409  .033 

 MF 

 (erector spinae) 

 Sphericity-

assumed 

 .222  2  Sphericity-

assumed .222 

2 

 9.843  .001  .314 

 Greenhouse-

Geisser 

 .222  1.258  .177  9.843  .005  .314 

 Huynh-Feldt  .222  1.344  .166  9.843  .004  .314 

 Lower limit  .222  1.000  .222  9.843  .009  .314 

 MPF 

 (erector spinae) 

 Sphericity-

assumed 

 .178  2  Sphericity-

assumed .178 

2 

 11.738  .000  .353 

 Greenhouse-

Geisser 

 .178  1.301  .136  11.738  .002  .353 

 Huynh-Feldt  .178  1.403  .127  11.738  .002  .353 

 Lower limit  .178  1.000  .178  11.738  .006  .353 

 ZCR 

 (erector spinae) 

 Sphericity-

assumed 

 .131  2  .066  4.082  .031  .159 

 Greenhouse-

Geisser 

 .131  1.486  .088  4.082  .047  .159 

 Huynh-Feldt  .131  1.664  .079  4.082  .040  .159 

 Lower limit  .131  1.000  .131  4.082  .068  .159 

 

 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

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Table 4. Post hoc tests for each indicator (Bonferroni correction) 

 Indicators  (I) Fatigue group  (J) Fatigue group  Mean 

difference (I-J) 

 Standard 

error 

 Significance  95% significant 

interval 

 Lower 

limit 

 Upper 

limit 

 RMS 

 (brachioradialis) 

 Low fatigue  Moderate Fatigue  -.015*  .004  .012  -.027  -.003 

 Severe fatigue  .027* -.007  .007  .011  -.048  -.006 

 Moderate fatigue  Severe fatigue  -.012* -.004  .004  .022  -.022  -.002 

 MF 

 (erector spinae) 

 Low fatigue  Moderate fatigue  .120*  .042  .035  .000  .239 

  Severe Fatigue  .191*  .056  .018  .032  .349 

 Moderate Fatigue  Severe fatigue  .071*  .026  .032  -.002  .144 

 MPF 

 (erector spinae) 

 Low fatigue  Moderate fatigue  .094*  .027  .017  .017  .172 

  Severe fatigue  .172*  .047  .011  .040  .304 

 Moderate Fatigue  Severe fatigue  .078*  .029  .015  -.004  .159 

 ZCR 

 (erector spinae) 

 Low fatigue  Moderate fatigue  .017  .033  1.000  -.077  .111 

  Severe fatigue  .136  .060  .137  -.034  .306 

  Moderate Fatigue  Severe fatigue  .119  .057  .188  -.043  .281 

 

 Table 5. Friedman non-parametric test for each indicator 

 Indicator  χ²  df  Sig.  Kendall's W 

 IEMG (brachioradialis)  24  2  .000  .727 

 MF (brachioradialis)  24  2  .000  .727 

 MPF (brachioradialis)  24  2  .000  .727 

 RMS (erector spinae muscle)  24  2  .000  .727 

 IEMG (erector spinae)  22.167  2  .000  .671 

 

Table 6. Wilcoxon signed rank test for each indicator 

 Indicator  Fatigue level  Z  Sig.  r 

 IEMG 

 (brachioradialis) 

 Moderate fatigue-low fatigue  -3.059  .002  -.883 

 Severe fatigue-low fatigue  -3.059  .002  -.883 

 Heavy fatigue-moderate fatigue  -3.059  .002  -.883 

 MF 

 (brachioradialis) 

 Moderate fatigue-low fatigue  -3.059  .002  -.883 

 Severe fatigue-low fatigue  -3.059  .002  -.883 

 Heavy fatigue-moderate fatigue  -3.059  .002  -.883 

 MPF 

 (brachioradialis) 

 Moderate fatigue - low fatigue  -3.059  .002  -.883 

 Severe fatigue-low fatigue  -3.059  .002  -.883 

 Heavy fatigue-moderate fatigue  -3.059  .002  -.883 

 RMS 

 (erector spinae) 

 Moderate fatigue-low fatigue  -3.059  .002  -.883 

 Severe fatigue-low fatigue  -3.059  .002  -.883 

 Heavy fatigue-moderate fatigue  -3.059  .002  -.883 

 IEMG 

 (erector spinae) 

 Moderate fatigue-low fatigue  -2.824  .005  -.815 

 Severe fatigue-low fatigue  -3.059  .002  -.883 

 Heavy fatigue-moderate fatigue  -3.059  .002  -.883 

 



X. Gui et al. /Future Sustainability                                                                                        November 2025| Volume 03 | Issue 04 | Pages 21-31 

30 

 

4. Conclusions 

In this paper, the fatigue state of construction workers 
was analyzed using EMG signals, and the fatigue significant 
indicators of EMG signals were screened and the following 
conclusions were drawn: 
(1) The brachioradialis and erector spinae muscles are 
common. During lifting operations, indicators of 
brachioradialis and erector spinae such as RMS and IEMG 
increased with increasing fatigue level, whereas MF and MPF 
decreased with increasing fatigue level, reflecting that as 
fatigue builds up, the muscle contraction efficiency decreases, 
resulting in the need for subjects to exert greater neural drive 
to maintain force output. Among them, the erector spinae 
muscle showed more obvious changes in EMG signal 
characteristics than the brachioradialis muscle, which was 
mainly attributed to the greater stability and strength support 
demands assumed in the lifting operation, and the percentage 
of fast muscle fibers and the activation pattern of high-
threshold motor units made its response to fatigue more 
significant. 
(2) Significant indicator screening. By statistically analyzing 
the characteristic indexes of EMG signals of brachioradialis 
and erector spinae, the RMS, IEMG, MF, and MPF indexes of 
brachioradialis and erector spinae had significant differences 
between different fatigue groups (P＜ 0.05), indicating that 
they can be used as core indexes for fatigue monitoring when 
evaluating EMG characteristics. 
 The study in this paper not only verifies the feasibility of EMG 
signals in occupational fatigue monitoring but also provides a 
scientific basis for the quantification of labor intensity, the 
optimization of operating posture, and the management of 
occupational health of construction workers. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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