Volume 12, Issue 2 (Summer 2025)
johe 2025, 12(2): 160-168 |
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Hosseini S A, Mashayekhi M, Shanbehzadeh A. Ergonomic Risk Assessment, Simulation, and Intervention Using Artificial Intelligence and CATIA Software: A Case Study in a Pathology Laboratory. johe 2025; 12 (2) :160-168
URL: http://johe.umsha.ac.ir/article-1-1016-en.html
URL: http://johe.umsha.ac.ir/article-1-1016-en.html
1- Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran
2- Student Research Committee, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
3- University of Rehabilitation and Social Welfare Sciences, Tehran, Iran ,azarshanbehzadeh59@gmail.com
2- Student Research Committee, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
3- University of Rehabilitation and Social Welfare Sciences, Tehran, Iran ,
Abstract: (1589 Views)
Background and Objective: Work-related musculoskeletal disorders are among the most common occupational health issues, with poor posture recognized as a major contributing factor. This study aimed to assess ergonomic risk at a workstation within a pathology laboratory, focusing on postural evaluation and intervention.
Materials and Methods: A microscopy workstation was selected for analysis. The initial posture was assessed using both the Copilot AI chatbot and CATIA V5 R21. Following an ergonomic intervention, the posture was re-simulated and re-evaluated using the Rapid Upper Limb Assessment (RULA) method. Then, pre- and post-intervention risk scores were compared.
Results: The initial RULA score was 7, indicating a high level of musculoskeletal risk. Forward trunk flexion and misalignment of the shoulders, arms, and thighs contributed to the high score. After implementing ergonomic improvements—specifically, redesigning the chair and correcting the sitting posture—the RULA score decreased to 3. Both tools (Copilot and CATIA) showed consistent agreement in posture evaluation before and after the intervention.
Conclusion: Integrating artificial intelligence chatbots with CATIA software proved effective in simulating and assessing workstation postures. These tools provide valuable support for evaluating ergonomic interventions before physical implementation.
Materials and Methods: A microscopy workstation was selected for analysis. The initial posture was assessed using both the Copilot AI chatbot and CATIA V5 R21. Following an ergonomic intervention, the posture was re-simulated and re-evaluated using the Rapid Upper Limb Assessment (RULA) method. Then, pre- and post-intervention risk scores were compared.
Results: The initial RULA score was 7, indicating a high level of musculoskeletal risk. Forward trunk flexion and misalignment of the shoulders, arms, and thighs contributed to the high score. After implementing ergonomic improvements—specifically, redesigning the chair and correcting the sitting posture—the RULA score decreased to 3. Both tools (Copilot and CATIA) showed consistent agreement in posture evaluation before and after the intervention.
Conclusion: Integrating artificial intelligence chatbots with CATIA software proved effective in simulating and assessing workstation postures. These tools provide valuable support for evaluating ergonomic interventions before physical implementation.
Keywords: Artificial Intelligence, Ergonomics, Intervention, Pathology Laboratory, Posture Assessment
Type of Study: Research Article |
Subject:
Ergonomics
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