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Pilot Study on the Relationship Between Acceptance of Collaborative Robots and Stress

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Abstract

Currently, collaborative robots (cobots) are mostly programmed to do one task repetitively. They can be programmed at different speeds and work near human operators. The goal of our research was to investigate the effect of robot speed on acceptance, subjective and objective stress, and cognitive workload of individuals. Therefore, we organized a repeated measures experiment in which participants (N = 25) conducted an assembly task with the YuMi cobot from ABB at a low and at a high speed. Subjective and physiological responses were collected, and participants were subjected to a standardized stress test. Our results indicate that when working with a cobot at a high speed, people believe they can work faster and be more productive but also experience a higher workload and higher perceived stress. We also found that tonic EDA is a significant physiological predictor for monitoring perceived stress in humans. We observed a greater relative increase in tonic EDA from baseline to task execution during high-speed mode compared to low-speed mode. Additionally, this increase in tonic EDA significantly correlated with participants’ perceived stress levels. However, workload could not be predicted by any of the physiological measures. Future research should explore the effect of higher cobot working speeds and the use of physiological measures (such as stress) as input to guide the collaboration between individuals and cobots.

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References

  1. Romero D, Mattsson S, Fast-Berglund Å et al (2018) Digitalizing occupational health, safety and productivity for the operator 4.0. IFIP Adv Inf Commun Technol 536:473–481. https://doi.org/10.1007/978-3-319-99707-0_59

    Article  Google Scholar 

  2. Bethel CL, Murphy RR (2010) Review of human studies methods in HRI and recommendations. Int J Soc Robot 2:347–359. https://doi.org/10.1007/s12369-010-0064-9

    Article  MATH  Google Scholar 

  3. Lovallo WR (2005) Stress and health: biological and psychological interactions. SAGE Publications, Inc. 2455 Teller Road, Thousand Oaks California 91320 United States

  4. Roelofs K (2017) Freeze for action: neurobiological mechanisms in animal and human freezing. Philos Trans R Soc B Biol Sci 372. https://doi.org/10.1098/rstb.2016.0206

  5. Bracha HS (2004) Freeze, flight, fight, fright, faint: adaptationist perspectives on the acute stress response spectrum. CNS Spectr 9:679–685. https://doi.org/10.1017/S1092852900001954

    Article  MATH  Google Scholar 

  6. Ziegler MG (2012) Psychological stress and the autonomic nervous system, third edit. Elsevier Inc

  7. Critchley HD (2002) Electrodermal responses: what happens in the brain. Neuroscientist 8:132–142. https://doi.org/10.1177/107385840200800209

    Article  MATH  Google Scholar 

  8. Boucsein W (2012) Electrodermal activity. Springer US, Boston, MA

    Book  Google Scholar 

  9. Posada-Quintero HF, Chon KH (2020) Innovations in electrodermal activity data collection and signal processing: a systematic review. Sensors 20:479

  10. Qasim MS, Bari DS, Martinsen ØG (2022) Influence of ambient temperature on tonic and phasic electrodermal activity components. Physiol Meas 43(6):065001. https://doi.org/10.1088/1361-6579/ac72f4

    Article  MATH  Google Scholar 

  11. Taelman J, Vandeput S, Spaepen A, Van Huffel S (2008) Influence of mental stress on heart rate and heart rate variability. IFMBE Proc 22:1366–1369. https://doi.org/10.1007/978-3-540-89208-3_324

    Article  MATH  Google Scholar 

  12. Giannakakis G, Grigoriadis D, Giannakaki K et al (2019) Review on psychological stress detection using biosignals. IEEE Trans Affect Comput 1–22. https://doi.org/10.1109/TAFFC.2019.2927337

  13. Arpaia P, Moccaldi N, Prevete R et al (2020) A wearable EEG instrument for real-time frontal asymmetry monitoring in worker stress analysis. IEEE Trans Instrum Meas 69:8335–8343. https://doi.org/10.1109/TIM.2020.2988744

    Article  MATH  Google Scholar 

  14. Leone A, Rescio G, Siciliano P et al (2020) Multi sensors platform for stress monitoring of workers in smart manufacturing context. In: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, pp 1–5

  15. Turner-Cobb JM, Asif M, Turner JE et al (2019) Use of a non-human robot audience to induce stress reactivity in human participants. Comput Hum Behav 99:76–85. https://doi.org/10.1016/j.chb.2019.05.019

    Article  MATH  Google Scholar 

  16. Agrigoroaie R, Tapus A (2020) Cognitive performance and physiological response analysis: analysis of the variation of physiological parameters based on user’s personality, sensory profile, and morningness–eveningness type in a human–robot interaction scenario. Int J Soc Robot 12:47–64. https://doi.org/10.1007/s12369-019-00532-z

    Article  Google Scholar 

  17. Toichoa Eyam A, Mohammed WM, Martinez Lastra JL (2021) Emotion-driven analysis and control of human-robot interactions in collaborative applications. Sensors 21:4626. https://doi.org/10.3390/s21144626

    Article  MATH  Google Scholar 

  18. Arai T, Kato R, Fujita M (2010) Assessment of operator stress induced by robot collaboration in assembly. CIRP Ann - Manuf Technol 59:5–8. https://doi.org/10.1016/j.cirp.2010.03.043

    Article  MATH  Google Scholar 

  19. Hopko SK, Khurana R, Mehta RK, Pagilla PR (2021) Effect of cognitive fatigue, operator sex, and robot assistance on task performance metrics, workload, and situation awareness in human-robot collaboration. IEEE Robot Autom Lett 6:3049–3056. https://doi.org/10.1109/LRA.2021.3062787

    Article  Google Scholar 

  20. Pollak A, Paliga M, Pulopulos MM et al (2020) Stress in manual and autonomous modes of collaboration with a cobot. Comput Hum Behav 112:106469. https://doi.org/10.1016/j.chb.2020.106469

    Article  MATH  Google Scholar 

  21. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478. https://doi.org/10.2307/30036540

    Article  MATH  Google Scholar 

  22. Dwivedi, Rana C, Williams (2011) A Meta-analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT). January:155–170. https://doi.org/10.1007/978-3-642-24148-2

  23. Panchetti T, Pietrantoni L, Puzzo G, Gualtieri L, Fraboni F (2023) Assessing the relationship between cognitive workload, Workstation Design, user Acceptance and Trust in Collaborative Robots. Appl Sci (Switzerland) 13(3). https://doi.org/10.3390/app13031720

  24. Van Der Elst W, Van Boxtel MPJ, Van Breukelen GJP, Jolles J (2006) The stroop color-word test: influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment 13:62–79. https://doi.org/10.1177/1073191105283427

    Article  Google Scholar 

  25. Vagas M, Galajdova A (2021) Application of speed and separation monitoring technique at automated assembly process. MM Sci J 2021(June):4420–4423. https://doi.org/10.17973/MMSJ.2021_6_2021036

    Article  MATH  Google Scholar 

  26. Hellhammer J, Schubert M (2012) The physiological response to Trier social stress test relates to subjective measures of stress during but not before or after the test. Psychoneuroendocrinology 37:119–124. https://doi.org/10.1016/j.psyneuen.2011.05.012

    Article  Google Scholar 

  27. Tarafdar M, Tu Q, Ragu-Nathan BS, Ragu-Nathan TS (2007) The impact of technostress on role stress and productivity. J Manag Inf Syst 24:301–328. https://doi.org/10.2753/MIS0742-1222240109

    Article  MATH  Google Scholar 

  28. Elprama SA, Vannieuwenhuyze JTA, De Bock S et al (2020) Social processes: what determines industrial workers’ intention to Use exoskeletons? Hum Factors. https://doi.org/10.1177/0018720819889534

    Article  Google Scholar 

  29. Nunnally JC (1978) Psychometric Theory 2nd ed

  30. Hart SG, Staveland LE (1988) Development of NASA-TLX (Task load index): results of empirical and theoretical research. In: Power Technology and Engineering. pp 139–183

  31. Hart SG (2006) Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(9), 904–908. https://doi.org/10.1177/154193120605000909

  32. Voorhees EE, Van, Dennis PA, Watkins LL, Patel TA, Calhoun PS, Dennis MF, Beckham JC (2022) Ambulatory heart rate variability monitoring: comparisons between the Empatica E4 wristband and Holter Electrocardiogram. 210–214. https://doi.org/10.1097/PSY.0000000000001010. March

  33. Kocielnik R, Sidorova N, Maggi FM et al (2013) Smart technologies for long-term stress monitoring at work. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems. IEEE, pp 53–58

  34. Benedek M, Kaernbach C (2010) A continuous measure of phasic electrodermal activity. J Neurosci Methods 190:80–91. https://doi.org/10.1016/j.jneumeth.2010.04.028

    Article  MATH  Google Scholar 

  35. Benedek M, Kaernbach C (2010) Decomposition of skin conductance data by means of nonnegative deconvolution. 47:647–658. https://doi.org/10.1111/j.1469-8986.2009.00972.x

  36. Filetti M (2020) Ledapy. https://github.com/HIIT/Ledapy

  37. R Core Team (2020) R: a lagnuage and environment for statistical computing

  38. Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67. https://doi.org/10.18637/jss.v067.i01

  39. Kuznetsova A, Brockhoff PB, Christensen RHB (2017) {lmerTest} Package: tests in linear mixed effects models. J Stat Softw 82:1–26. https://doi.org/10.18637/jss.v082.i13

    Article  MATH  Google Scholar 

  40. Nakagawa S, Johnson PCD, Schielzeth H (2017) The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J R Soc Interface 14. https://doi.org/10.1098/rsif.2017.0213

  41. Bartoń K (2020) {MuMin} Package: Mulit-Model Inference. J Stat Softw

  42. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 19:716–723. https://doi.org/10.1109/TAC.1974.1100705

    Article  MathSciNet  MATH  Google Scholar 

  43. Schwarz G (1978) Estimating the dimention of a model. Ann Stat 6:461–464

  44. Fox J, Weisberg S (2019) An {R} companion to Applied Regression, Third. Sage, Thousand Oaks {CA}

    MATH  Google Scholar 

  45. Meissner A, Trübswetter A, Conti-Kufner AS, Schmidtler J (2020) Friend or foe understanding assembly workers’ acceptance of human-robot collaboration. ACM Trans Human-Robot Interact 10:1–30. https://doi.org/10.1145/3399433

    Article  Google Scholar 

  46. Wijsman J, Grundlehner B, Liu H et al (2013) Wearable physiological sensors reflect mental stress state in office-like situations. Proc – 2013 Hum Assoc Conf Affect Comput Intell Interact ACII 2013 600–605. https://doi.org/10.1109/ACII.2013.105

  47. Smets E, Casale P, Großekathöfer U et al (2016) Comparison of machine learning techniques for psychophysiological stress detection. In: International Symposium on Pervasive Computing Paradigms for Mental Health. Springer, pp 13–22

  48. Posada-Quintero HF, Florian JP, Orjuela-Cañón AD et al (2016) Power spectral density analysis of electrodermal activity for sympathetic function assessment. Ann Biomed Eng 44:3124–3135. https://doi.org/10.1007/s10439-016-1606-6

    Article  MATH  Google Scholar 

  49. Pakarinen T, Pietila J, Nieminen H (2019) Prediction of self-perceived stress and arousal based on electrodermal activity∗. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 2191–2195. https://doi.org/10.1109/EMBC.2019.8857621

  50. Kalimeri K, Saitis C (2016) Exploring multimodal biosignal features for stress detection during indoor mobility. ICMI 2016 - Proc 18. ACM Int Conf Multimodal Interact 53–60. https://doi.org/10.1145/2993148.2993159

  51. Yan S, Tran CC, Wei Y, Habiyaremye JL (2019) Driver’s mental workload prediction model based on physiological indices. Int J Occup Saf Ergon 25:476–484. https://doi.org/10.1080/10803548.2017.1368951

    Article  Google Scholar 

  52. Chanel CPC, Roy RN, Dehais F, Drougard N (2020) Towards mixed-initiative human–robot interaction: assessment of discriminative physiological and behavioral features for performance prediction. Sens (Switzerland) 20. https://doi.org/10.3390/s20010296

  53. Healey JA, Picard RW (2005) Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transp Syst 6:156–166. https://doi.org/10.1109/TITS.2005.848368

    Article  MATH  Google Scholar 

  54. Setz C, Arnrich B, Schumm J et al (2010) Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans Inf Technol Biomed 14:410–417. https://doi.org/10.1109/TITB.2009.2036164

    Article  MATH  Google Scholar 

  55. Kurniawan H, Maslov AV, Pechenizkiy M (2013) Stress detection from speech and galvanic skin response signals. Proc 26th IEEE Int Symp Comput Med Syst 209–214. https://doi.org/10.1109/CBMS.2013.6627790

  56. Smets E, Velazquez ER, Schiavone G et al (2018) Large-scale wearable data reveal digital phenotypes for daily-life stress detection. https://doi.org/10.1038/s41746-018-0074-9

  57. Braithwaite JJ, Watson DG, Jones R, Rowe M (2015) A guide for Analysing Electrodermal Activity (EDA) & skin conductance responses. (SCRs) for Psychological Experiments

  58. Zhang Z, Pi Z, Liu B (2015) TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Trans Biomed Eng 62:522–531. https://doi.org/10.1109/TBME.2014.2359372

    Article  MATH  Google Scholar 

  59. Levenson RW (2014) The autonomic nervous system and emotion. Emot Rev 6:100–112. https://doi.org/10.1177/1754073913512003

    Article  MATH  Google Scholar 

  60. Grimley SJ, Ko CM, Morrell HER et al (2019) The need for a neutral speaking period in psychosocial stress testing. J Psychophysiol 33:267–275. https://doi.org/10.1027/0269-8803/a000228

    Article  MATH  Google Scholar 

  61. Thibault R, Goujon N, Le Gallic E, Clairand R, Sébille V, Vibert J, Schneider SM, Darmaun D (2009) Use of 10-point analogue scales to estimate dietary intake: a prospective study in patients nutritionally at-risk. Clin Nutr 28(2):134–140. https://doi.org/10.1016/j.clnu.2009.01.003

    Article  Google Scholar 

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Acknowledgements

We thank VLAIO for supporting this research under the “Living Labs Smart Factories” (HBC.2017.0801) and we thank the European Union’s Horizon 2020 Research and Innovation Programme (H2020-ICT-2019-2/ 2019–2023) for supporting this work with funding for EU Sophia under grant agreement No. 871237. This work was supported by a PhD fellowship from the Research Foundation - Flanders (FWO) awarded to EL (1SB4719N).

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Erika Lutin: Conceptualization, Methodology, Formal analysis, Investigation, Writing - Original Draft, Visualization. Shirley Elprama: Conceptualization, Methodology, Formal analysis, Writing - Original Draft. Jan Cornelis: Conceptualization, Investigation, Data curation, Visualization, Writing - Original Draft. Patricia Leconte, Bart Van Doninck & Maarten Witters: Conceptualization, Investigation, Project administration, Resources, Writing – Review & Editing. Walter De Raedt: Conceptualization, Project administration, Writing – Review & Editing. An Jacobs: Conceptualization, Project administration, Writing – Review & Editing.

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Correspondence to Shirley A. Elprama.

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Lutin, E., Elprama, S.A., Cornelis, J. et al. Pilot Study on the Relationship Between Acceptance of Collaborative Robots and Stress. Int J of Soc Robotics 16, 1475–1488 (2024). https://doi.org/10.1007/s12369-024-01156-8

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