Assessment and Prediction of Industrial Workers’ Fatigue in an Overhead Assembly Job

Binoosh, S.A.; Mohan, G. Madhan; Bijulal, D.

doi:10.7166/28-1-1697

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…Xu et al (2014) demonstrated that age has no significant effect on the movement time during machine-paced assembly tasks. According to Binoosh, Mohan, and Bijulal (2017), although older and more experienced groups of workers showed significantly increased fatigue, this appears not to affect their performance more than that of younger workers.…”

Section: Analysis and Evaluation Of Ageing Workers' Functional Capacimentioning

confidence: 94%

Ageing workforce management in manufacturing systems: state of the art and future research agenda

Calzavara

Battini

Bogataj

et al. 2019

International Journal of Production Research

140

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The workforce ageing phenomenon is recently affecting most of the Organisation for Economic Co-operation and Development (OECD) member countries, due to a general ageing of their populations and a higher average retirement age of the workforce. In this paper, the topic of ageing workforce management is addressed from a production research standpoint, with the aim of understanding how older workers can be supported and involved in a manufacturing system. First, the current state of the art related to the ageing workforce in production systems is presented. This is structured according to four main topics: (1) analysis and evaluation of ageing workers' functional capacities, (2) consideration of ageing workers' capacities in industrial system modelling and management, (3) analysis and exploitation of ageing workers' expertise, (4) acknowledgement, analysis, design and integration of supporting technologies. Next, the discussion on the impact of the ageing workforce on manufacturing systems' performances leads to the comparison of some technological advances that are related to the Industry 4.0 paradigms. Finally, a future research agenda on this topic is proposed, based on the same topics classification proposed for the literature analysis. Five different research areas are derived, suggesting future directions for appropriate research concerning the employ of older workers in production environments.

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Section: Analysis and Evaluation Of Ageing Workers' Functional Capacimentioning

confidence: 94%

Ageing workforce management in manufacturing systems: state of the art and future research agenda

Calzavara

Battini

Bogataj

et al. 2019

International Journal of Production Research

140

View full text Add to dashboard Cite

show abstract

“…The tools were applied to confirm that the driving tasks induced the participants' fatigue. The subjective measurements were SOFI [11], the KSS [7][8], and the subjective Fatigue Rating [9][10]. SOFI was applied to measure the subjective fatigue before and after driving using a scale of 0 = not at all to 6 = very high degree, with 3 as an intermediary level.…”

Section: Subjective Data Collectionmentioning

confidence: 99%

“…According to reports in the literature, physiological changes can be measured as fatigue indicators using both subjective and objective tools [5,6]. The subjective tools attempt to capture self-perception of the sleepiness/fatigue level at a certain time based on standardized questionnaires, such as the Karolinska Sleepiness Scale (KSS), the Stanford Sleepiness Scale (SSS), the Epworth Sleepiness Scale (EPS), the Fatigue Visual Analogue Scale (F-VAS), the Fatigue Rating, and the Swedish Occupational Fatigue Index (SOFI) [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of EEG Changes during Prolonged Simulated Driving for the Assessment of Driver Fatigue

Zuraida¹,

Iridiastadi²,

Sutalaksana³

et al. 2019

J. Eng. Technol. Sci.

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Fatigue during driving is the main contributing factor to road accidents. It is influenced by time on task (TOT) and time of day (TOD). Recent electroencephalogram (EEG) research on fatigue assessment has shown a promising result in explaining the fatigue phenomenon. However, different findings exist regarding the best EEG parameters related to fatigue. This study examined EEG changes according to the effect of TOT and TOD and determined the best parameters to distinguish fatigue status. To generate driver fatigue, prolonged driving in the morning and at night in a simulator was conducted. The EEG signal was collected from 28 male participants at frontal and occipital areas. The EEG power (brainwave) was determined from the first and last 5 minutes of the driving task and after a break of 30 minutes. The results of this study showed a general tendency of EEG power changing throughout the driving sessions. However, changes related to fatigue were only found for the night sessions, as confirmed by θ power and the subjective fatigue measurement result. This study showed that TOT (as a factor that induces fatigue) was explained by θ from the frontal area, whereas TOD was differentiated by α, θ, θ/β, (θ+α)/β and (θ+α)/(β+α).

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“…However, some studies showed opposite results under drowsy conditions. Byeon et al 51 and Patel et al 52 studied driving fatigue in ECG signals, and a marked decrease was shown in the LF to HF ratio in drivers’ fatigue state, while other studies 4 , 53 showed that LF/HF may increase with higher fatigue levels during tasks. Therefore, the results of these studies are still in dispute.…”

Section: Discussionmentioning

confidence: 98%

“…Huang et al 3 analyzed the applicability of “Subjective Fatigue Symptoms” revised by the Japan Institute of Industrial Hygiene in the Chinese Manufacturing Industry. Binoosh et al 4 designed the assembly test of workers and used several fatigue questionnaires to predict the fatigue degree of workers. The results show that the Borg CR10 scale has better performance than the Samn-Perelli fatigue scale (SPFS) in fatigue predictability.…”

Section: Introductionmentioning

confidence: 99%

Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis

Ruitao

Yan

Duan

et al. 2022

Sci Rep

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In recent years, fatigue has become an important issue in modern life that cannot be ignored, especially in some special occupations. Agricultural workers are high-risk occupations that, under fatigue conditions over a long period, will cause health problems. In China, since very few studies have focused on the fatigue state of agricultural workers, we were interested in using electrocardiogram (ECG) signals to analyze the fatigue state of agricultural workers. Healthy agricultural workers were randomly recruited from hilly orchards in South China. Through the field experiment, 130 groups of 5-min interval ECG signals were collected, and we analyzed the ECG signal by HRV. The time domain (meanHR, meanRR, SDNN, RMSSD, SDSD, PNN20, PNN50 and CV), frequency domain (VLF percent, LF percent, HF percent, LF norm, HF norm and LF/HF) and nonlinear parameters (SD1, SD2, SD1/SD2 and sample entropy) were calculated and Spearman correlation coefficient analysis and Mann–Whitney U tests were performed on each parameter for further analysis. For all subjects, nine parameters were slightly correlated in nonfatigue and fatigue state. Six parameters were significantly increased and ten HRV parameters were significantly decreased compared the nonfatigue state. As for males, fifteen parameters were significantly different, and for females, eighteen parameters were significantly different. In addition, the probability density functions of SDNN, SDSD, VLF%, HFnorm and LF/HF were significantly different in nonfatigue and fatigue state for different genders, and the nonlinear parameters become more discrete compared the nonfatigue state. Finally, we obtained the most suitable parameters, which reflect the fatigue characteristics of orchard workers under different genders. The results have instructional significance for identifying fatigue in orchard workers and provide a convincing and valid reference for clinical diagnosis.

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Assessment and Prediction of Industrial Workers’ Fatigue in an Overhead Assembly Job

Cited by 8 publications

References 28 publications

Ageing workforce management in manufacturing systems: state of the art and future research agenda

Ageing workforce management in manufacturing systems: state of the art and future research agenda

An Analysis of EEG Changes during Prolonged Simulated Driving for the Assessment of Driver Fatigue

Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis

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