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Understanding Workers' Well-Being and Cognitive Load in Human-Cobot Collaboration: Systematic Review.

Giulia Bassi1,2, Valeria Orso2,3, Silvia Salcuni1

  • 1Department of Developmental Psychology and Socialization, University of Padua, Padova, Italy.

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This summary is machine-generated.

Collaborative robots (cobots) in manufacturing can increase stress and anxiety due to job security fears and fast operations. Further research in real-world settings is needed to ensure cobot integration supports worker well-being.

Keywords:
Industry 5.0affective well-beingcobotscognitive workloadcollaborative robotsmanufacturingoperatorssystematic review

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Area of Science:

  • Human-robot interaction
  • Industrial engineering
  • Occupational psychology

Background:

  • Industry 5.0 prioritizes human well-being alongside technological advancements.
  • Collaborative robots (cobots) are reshaping manufacturing processes.
  • A systematic review is needed on cobot impact on operator affective well-being and cognitive workload.

Purpose of the Study:

  • To systematically review empirical studies on affective well-being (anxiety, stress, depression) and cognitive workload in human-cobot collaboration (HCC).
  • To address the gap in literature focusing on psychological dimensions of cobot integration.
  • To investigate factors influencing affective well-being and cognitive workload in industrial HCC.

Main Methods:

  • Comprehensive literature search across multiple databases (Web of Science, Scopus, ACM, IEEE Xplore).
  • Inclusion of peer-reviewed empirical studies reporting quantitative or qualitative data on cognitive workload or affective well-being in HCC.
  • Independent study selection and data extraction by two reviewers.

Main Results:

  • A significant increase in HCC publications since 2020.
  • Most studies (61%) were conducted in lab settings, indicating a gap in real-world industrial research.
  • Cobots can alleviate physical fatigue but may increase stress and anxiety due to job security concerns and high-paced operations. Factors like cobot speed, proximity, usability, and task complexity influence well-being and workload.
  • Higher task complexity significantly increases cognitive workload and stress levels, as evidenced by physiological and self-report measures.

Conclusions:

  • Key factors influencing operator affective well-being and cognitive workload in cobot collaboration have been identified.
  • Findings can guide future research and interventions to ensure cobot integration supports both productivity and operator well-being in manufacturing.
  • Future studies should prioritize real-world settings, standardized assessments, physiological measures, and qualitative interviews for effective implementation.