This paper explores the effectiveness of various cognitive workload metrics in the context of human- robot collaborative assembly tasks. Through a comprehensive meta-analysis and systematic literature review, we examined the validity and sensitivity of three categories of metrics: physiological (EEG, GSR, HRV), subjective (NASA-TLX), and behavioral measures. The study aimed to ascertain which metrics provide the most reliable indicators of cognitive load in these settings. Results demonstrate that while each category has its strengths, integrating multiple metrics can significantly enhance the accuracy of cognitive load assessments. This integration is crucial in adapting to the complexities and dynamic interactions characteristic of Industry 5.0 environments, where human well-being and efficient interaction with robots are paramount. The findings advocate for a multimodal approach to workload assessment, ensuring both operator safety and efficiency in advanced manufacturing systems.

Evaluating Mental Workload Measures in Human-Robot Collaborative Assembly

Xiranai Dai;Gaia Vitrano
2024-01-01

Abstract

This paper explores the effectiveness of various cognitive workload metrics in the context of human- robot collaborative assembly tasks. Through a comprehensive meta-analysis and systematic literature review, we examined the validity and sensitivity of three categories of metrics: physiological (EEG, GSR, HRV), subjective (NASA-TLX), and behavioral measures. The study aimed to ascertain which metrics provide the most reliable indicators of cognitive load in these settings. Results demonstrate that while each category has its strengths, integrating multiple metrics can significantly enhance the accuracy of cognitive load assessments. This integration is crucial in adapting to the complexities and dynamic interactions characteristic of Industry 5.0 environments, where human well-being and efficient interaction with robots are paramount. The findings advocate for a multimodal approach to workload assessment, ensuring both operator safety and efficiency in advanced manufacturing systems.
2024
Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management
Human-Robot Collaboration, Mental Workload, Assembly Tasks, Physiological Measures, Meta-Analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1281432
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