A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

Kavus, Bahar Yalcin; Tas, Pelin Gulum; Gümüş, Alev Taşkın

doi:10.1016/j.engappai.2023.106373

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…Rapid entire body assessment evaluates the risk of musculoskeletal disorders associated with specific tasks within each job [20]. Thus, multiple studies have proposed using REBA to improve workers' muscle injuries in many job fields, especially Implementing a Risk Assessment System of Electric Welders' Muscle Injuries for Working Posture Detection with AI Technology in the industrial sector [21][22]. REBA assessment determines the postural angles of six body parts: the neck, trunk, legs, shoulders, arms, and wrists.…”

Section: Ergonomics Assessment With Rebamentioning

confidence: 99%

Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology

Ruengdech,

Howimanporn,

Intarakumthornchai

et al. 2024

Int. J. Onl. Eng.

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Maintaining health and safety is essential for workers’ quality of life, and thus, this has become one of the main priorities for industrial enterprises. Electric welders want required safety precautions to be implemented during work in industries with safety risks, especially muscle injuries. This challenge needs to be addressed by the safety officer, who should suggest a way to decrease the risk for workers. However, traditional assessment based on human evaluation and the need for expertise and accuracy in risk assessment have produced muscle injuries. Thus, using artificial intelligence (AI) technology to mitigate risk assessment is cost-effective and accurate. This study proposed a risk assessment system for muscle injuries (RASMI) with AI technology to assess electric welder postures with rapid entire body assessment (REBA) standards to identify the cause of muscle injuries and to warn electric welders when their pose may be a risk. The findings showed that the system can effectively and precisely evaluate the risk assessment of electric welders’ muscle injuries. Additional results showed that they perceive using AI technology to enhance wellness positively in terms of working with warnings for posture adjustment or behavior that can significantly affect an operator’s long-term health and well-being.

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Section: Ergonomics Assessment With Rebamentioning

confidence: 99%

Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology

Ruengdech,

Howimanporn,

Intarakumthornchai

et al. 2024

Int. J. Onl. Eng.

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“…Table 5 provides a concise summary of the ergonomic risk assessment performed in the context of specific subtasks and the positions of the evaluated teachers. REBA is a widely used tool due to its convenience and consideration of all parts of the body [12]. In this way, the ERGO/IBV software is used, which reflects the results of the risk assessment according to the REBA method.…”

Section: Ergonomic Risk Assessment Using the Ergo/ibv Softwarementioning

confidence: 99%

“…It must be taken into account that musculoskeletal disorders are among the most widespread occupational health problems caused by non-ergonomic working conditions in industrialized countries. In 2017, there were 138.7 million disability-adjusted life years, 1.3 billion extensive cases, and 121,300 deaths worldwide associated with problems related to musculoskeletal disorders [12].…”

Section: Ergonomic Risk Assessment Using the Ergo/ibv Softwarementioning

confidence: 99%

“…The characteristics of ergonomic risk assessment include identifying ergonomic hazards in the workplace, assessing the risk associated with these hazards, and recommending interventions with which to eliminate or reduce the risk of work-related musculoskeletal disorders. Ergonomic risk assessment often involves the use of standardized tools, such as NIOSH, RULA, OWAS, REBA, and WISHA, proposed for different working conditions and focusing on various parts of the human body [12].…”

Section: Ergonomic Risk Assessment Using the Ergo/ibv Softwarementioning

confidence: 99%

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Musculoskeletal Disorders in Primary School Teachers

Ramírez-García,

Lluguay-Quispillo,

Inga-Lafebre

et al. 2023

Sustainability

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Teachers in schools often face working conditions imposed by educational institutions that can pose ergonomic risks and affect their health. This research aims to determine the presence of musculoskeletal disorders and the ergonomic risk factors associated with this population. We collected anthropometric data, assessed musculoskeletal symptoms using the Nordic Kuorinka questionnaire in 134 teachers, and analyzed their workplaces using the ERGO/IBV software. The results include an anthropometric description, highlighting that 94% of women and 87% of men are overweight or obese. It also shows the presence of musculoskeletal discomfort, with 69% and 49% of teachers reporting neck and back/lumbar pain, respectively. Among the main attributions to these discomforts, teachers mention that repetitive tasks, improper postures, and stress are the causes. Finally, the results of the ergonomic assessment are mentioned. Using the rapid entire body assessment method, a high and very high risk level was identified for each subtask, while in respect to repetitive tasks, the neck was identified as the most critical area due to the activities performed. In conclusion, we found an association between back/lumbar pain and body mass index, suggesting that overweight or obesity may increase the risk of musculoskeletal disorders. Additionally, cervical pain is directly related to the repetitive tasks performed by teachers.

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A assessment method for ergonomic risk based on fennec fox optimization algorithm and generalized regression neural network

Pei,

Cao,

Ding

et al. 2025

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A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

Cited by 5 publications

References 36 publications

Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology

Implementing a Risk Assessment System of Electric Welders’ Muscle Injuries for Working Posture Detection with AI Technology

Musculoskeletal Disorders in Primary School Teachers

A assessment method for ergonomic risk based on fennec fox optimization algorithm and generalized regression neural network

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