1- Hamadan University of Medical Sciences
2- Isfahan University of Medical Sciences
3- University of Rehabilitation and Social Welfare ,azarshanbehzadeh59@gmail.com
2- Isfahan University of Medical Sciences
3- University of Rehabilitation and Social Welfare ,
Abstract: (66 Views)
Background and Objective: Work-related musculoskeletal disorders are among the most prevalent 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.
Methods: A microscopy workstation was selected for analysis. The initial posture was assessed using both the Copilot AI chatbot and CATIA software. Following an ergonomic intervention, the posture was re-simulated and re-evaluated using the Rapid Upper Limb Assessment (RULA) method. Pre- and post-intervention risk scores were then 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 elevated 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: The combined use of artificial intelligence chatbots and CATIA software proved effective for simulating and assessing workstation postures. These tools offer valuable support for evaluating ergonomic interventions prior to physical implementation.
Methods: A microscopy workstation was selected for analysis. The initial posture was assessed using both the Copilot AI chatbot and CATIA software. Following an ergonomic intervention, the posture was re-simulated and re-evaluated using the Rapid Upper Limb Assessment (RULA) method. Pre- and post-intervention risk scores were then 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 elevated 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: The combined use of artificial intelligence chatbots and CATIA software proved effective for simulating and assessing workstation postures. These tools offer valuable support for evaluating ergonomic interventions prior to physical implementation.
Article number: 6
Type of Study: Research Article |
Subject:
Ergonomics
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