Journal of Health and Safety at Work

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Introduction: Physical fatigue is one of the major risk factors for work-related musculoskeletal disorders and has many life and financial costs. The impact of physical/biomechanical, psychosocial, environmental, and individual risk factors on muscle fatigue is undeniable. The aim of this study is to model the phenomenon of muscle fatigue (as output) in the hand in work environments based on these risk factors (as input) using soft computing methods.
Material and Methods: In the first step, associated risk factors of fatigue for 156 subjects (in three job categories) were assessed using Copenhagen environmental, psychosocial, demographic, and Man-TRA tools. Then, the Roman-Liu equation and mean square amplitude of acceleration waves were used to measure fatigue with a dynamometer and a three-axis accelerometer, respectively. Finally, according to the nature of risk factors and the phenomenon of fatigue, six categories (24 methods) of supervised machine learning (SML) based on classification were selected. MatLab software (MatLab R2017b, The Mathworks Inc., MA, U.S.A.) was used to fit the models using SML.
Results: The best-fitted models in the first and second half of the work shift were obtained using support vector machine methods. Physical risk factors had a significant impact on physical fatigue. After filtering low-priority risk factors, in the first half of the work shift, the most optimal model had an accuracy of 71.8%, precision of 72.5%, sensitivity of 76.9%, specificity of 70.8%, and discrimination power equal to 73%. In the second half of the work shift, the accuracy, precision, sensitivity, and specificity of the optimized model were 60.3%, 57.5%, 50%, and 46.9%, respectively, and the discrimination power was obtained at about 62%.
Conclusion: The fitted models for hand fatigue had acceptable performance in both sections of the shift but can still be optimized. Therefore, it is necessary for future studies to improve the quality of input and output data and include other dimensions affecting fatigue such as cognitive workload and type of work shift in future models.

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Introduction: In many occupations, users must sit for prolonged periods during their job activities. Prolonged sitting is associated with fatigue, leading to postural changes that can increase spinal loads. Despite the importance of this topic in terms of the extent of prolonged sitting and its subsequent adverse consequences, little attention has been given to this occupational activity. Hence, this study investigates changes in neck, trunk, and muscle activities and fatigue levels in prolonged sitting computer tasks.
Material and Methods: Twenty healthy subjects (gender-balanced) from the student community with at least five years of experience in computer work aged between 20-30 years were asked to randomly perform three types of computer tasks for 90 minutes (each task for 30 minutes). Electromyographic (EMG) activities of right and left cervical (ESCR and ESCL), thoracic (ESTR and ESTL), and lumbar (ESLR and ESLL) erector spine and upper trapezius (UTR and UTL) muscles were continuously recorded. Root mean square (RMS) and median frequency were extracted as EMG metrics. Subjects also rated their perceived discomfort using a Visual Analogue Scale (VAS). The effect of time, gender, and their interaction on muscle EMG activities, fatigue, and discomfort were explored.
Results: Time had a statistically significant effect on UTR, ESCR, and ESTR muscle activities. UTL and ESCR muscle activities significantly differed between male and female subjects. Further, the findings confirmed the interactive effect of time and gender on ESTR muscle activity. UTR, ESCR, ESCL, ESTL, and ESLL muscles’ fatigue index changed statistically over time.
Conclusion: The findings confirmed neck and trunk muscles’ fatigue by increasing muscular activity and reducing frequency contents over time, per the subjective rating of discomfort.