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Workplace design plays a crucial role in manufacturing industries, impacting worker well-being and productivity. Biomechanical load management represents a significant concern in these industries, with ergonomic interventions such as workplace redesign, tool and equipment modifications, training, job rotations being key methods to reduce this load. Among these methods, workplace redesign has displayed significant potential for biomechanical load management in various studies. To ascertain safety and comfort of all the workers while designing a workstation, anthropometrical considerations play an important role. However, given the vast array of anthropometric dimensions and considerations, there is a recognized gap in comprehensive understanding of the synergy in relevant physical attributes and postural demands of the specific manufacturing tasks for workplace design. Hence, this study investigates key anthropometric features influencing ergonomic exposure in a specific manufacturing task using different workplace setups. Ergonomic exposure corresponding to each setup was estimated based on RULA criterion using inertial motion capture system. Ten participants performed a kitting operation for a pneumatic cylinder assembly in the CEFC smart factory in separate sessions with different workplace setups. Anthropometric features were systematically evaluated to assess their impact on the overall ergonomic exposure estimate in different workplace setups using correlation-based feature selection (CFS). The priority rankings of these features obtained in different workplace setups were then aggregated using Copeland’s method to pinpoint the most relevant anthropometric features. Forearm length, stature, eye height, and elbow height were identified as significant predictors of biomechanical load. The critical dimensions for an ergonomic workstation were calculated considering the 5th to 95th percentile of Indian manufacturing worker anthropometric data, based on identified anthropometric features. Future work includes utilizing the identified features to develop a predictive model of biomechanical load to facilitate workstation design. The findings contribute to enabling informed workplace redesign decisions considering anthropometric variation among the user population.

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Empowering Workplace Design: Anthropometric Feature Selection Through Inertial Motion Capture-Based Ergonomic Assessment

  • 会议论文

  • 发表日期：

    2025年

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  • Charu Tripathi,
  • Manish Arora,
  • Amaresh Chakrabarti

摘要

Workplace design plays a crucial role in manufacturing industries, impacting worker well-being and productivity. Biomechanical load management represents a significant concern in these industries, with ergonomic interventions such as workplace redesign, tool and equipment modifications, training, job rotations being key methods to reduce this load. Among these methods, workplace redesign has displayed significant potential for biomechanical load management in various studies. To ascertain safety and comfort of all the workers while designing a workstation, anthropometrical considerations play an important role. However, given the vast array of anthropometric dimensions and considerations, there is a recognized gap in comprehensive understanding of the synergy in relevant physical attributes and postural demands of the specific manufacturing tasks for workplace design. Hence, this study investigates key anthropometric features influencing ergonomic exposure in a specific manufacturing task using different workplace setups. Ergonomic exposure corresponding to each setup was estimated based on RULA criterion using inertial motion capture system. Ten participants performed a kitting operation for a pneumatic cylinder assembly in the CEFC smart factory in separate sessions with different workplace setups. Anthropometric features were systematically evaluated to assess their impact on the overall ergonomic exposure estimate in different workplace setups using correlation-based feature selection (CFS). The priority rankings of these features obtained in different workplace setups were then aggregated using Copeland’s method to pinpoint the most relevant anthropometric features. Forearm length, stature, eye height, and elbow height were identified as significant predictors of biomechanical load. The critical dimensions for an ergonomic workstation were calculated considering the 5th to 95th percentile of Indian manufacturing worker anthropometric data, based on identified anthropometric features. Future work includes utilizing the identified features to develop a predictive model of biomechanical load to facilitate workstation design. The findings contribute to enabling informed workplace redesign decisions considering anthropometric variation among the user population.