Abstract
A methodology is presented to obtain measurements of the emotional states of workers from the measurement of Heart Rate Variability. Two methodologies have been used, one based on logistic regression and another using fuzzy trees. The results show promising results to have a single model for using through different persons to obtain an estimation of their internal arousal and valence. This estimation will be validated in a second stage with a measurement of the cognitive load of the worker.
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References
Peruzzini, M., Grandi, F., Pellicciari, M.: Benchmarking of tools for user experience analysis in industry 4.0. Procedia Manuf. 11, 806–813 (2017)
Scott, K.M., Lim, C., Al-Hamzawi, A., Alonso, J., Bruffaerts, R., Caldas-de-Almeida, J.M., Florescu, S., de Girolamo, G., Hu, C., de Jonge, P., Kawakami, N., Medina-Mora, M.E., Moskalewicz, J., Navarro-Mateu, F., O’Neill, S., Piazza, M., Posada-Villa, J., Torres, Y., Kessler, R.C.: Association of mental disorders with subsequent chronic physical conditions: world mental health surveys from 17 countries. J. Am. Med. Assoc. psychiatry 73(2), 150–158 (2016)
Gillen, M., Yen, I.H., Trupin, L., Swig, L., Rugulies, R., Mullen, K., Font, A., Burian, D., Ryan, G., Janowitz, I., Quinlan, P.A., Frank, J., Blanc, P.: The association of socioeconomic status and psychosocial and physical workplace factors with musculoskeletal injury in hospital workers. Am. J. Ind. Med. 50(4), 245–260 (2007)
De Wind, A., Geuskens, G.A., Reeuwijk, K.G., Westerman, M.J., Ybema, J.F., Burdorf, A., Bongers, P.M., Van der Beek, A.J.: Pathways through which health influences early retirement: a qualitative study. BMC Public Health 13(1), 292 (2013)
Engström, J., Johansson, E., Östlund, J.: Effects of visual and cognitive load in real and simulated motorway driving. Transp. Res. Part F: Traffic psychol. Behav. 8(2), 97–120 (2005)
Fairclough, S.H., Venables, L., Tattersall, A.: The influence of task demand and learning on the psychophysiological response. Int. J. Psychophysiol. 56(2), 171–184 (2005)
Fairclough, S.H., Venables, L.: Prediction of subjective states from psychophysiology: a multivariate approach. Biol. Psychol. 71(1), 100–110 (2006)
Cohen, R.A., Waters, W.F.: Psychophysiological correlates of levels and stages of cognitive processing. Neuropsychologia 23(2), 243–256 (1985)
Scheirer, J., Fernandez, R., Klein, J., Picard, R.W.: Frustrating the user on purpose: a step toward building an affective computer. Interact. Comput. 14(2), 93–118 (2002)
Jorgensen, R.S., Johnson, B.T., Kolodziej, M.E., Schreer, G.E.: Elevated blood pressure and personality: a meta-analytic review. Psychol. Bull. 120(2), 293 (1996)
Wagner, J., Kim, J., Andre, E.: From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification. In: 2005 IEEE International Conference on Multimedia and Expo, Amsterdam, pp. 940–943 (2005)
Appelhans, B., Luecken, L.: Heart rate variability as an index of regulated emotional responding. Rev. Gen. Psychol. 10, 229–240 (2006). https://doi.org/10.1037/1089-2680.10.3.229
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (iaps): affective ratings of pictures and instruction manual. Technical Report A-8 (2008)
Pan, J., Tompkins, W.J.: A real-time qrs detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985)
MATLAB. https://es.mathworks.com/help/wavelet/examples/wavelet-packets-decomposing-the-details.html
Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets Syst. 69(2), 125–39 (1995). https://doi.org/10.1016/0165-0114(94)00229-Z
Nardelli, M., Greco, A., Valenza, G., Lanata, A., Bailón, R., Scilingo, E.P.: A multiclass arousal recognition using HRV nonlinear analysis and affective images. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 392–395, Honolulu, HI (2018)
Lench, H.C., Flores, S.A., Bench, S.W.: Discrete emotions predict changes in cognition, judgment, experience, behavior, and physiology: a meta-analysis of experimental emotion elicitations. Psychol. Bull. 137, 834–855 (2011)
Amstadter, A.: Emotion regulation and anxiety disorders. J. Anxiety. Disord. 22, 211–221 (2008)
Kroenke, K., Spitzer, R.L., Williams, J.B.: The patient health questionnaire-2: validity of a two-item depression screener. Medicalcare 41(11), 1284–1292 (2003)
Lee, C.K., Yoo, S.K., Park, Y.J., Kim, N.H.: Using neural network to recognize human emotions from heart rate variability and skin resistance. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 5523–5525 (2005)
Kim, K.H., Bang, S.W., Kim, S.R.: Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Compute. 42, 419–427 (2004)
Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1175–1191 (2001)
Yu, S.N., Chen, S.F.: Emotion state identification based on heart rate variability and genetic algorithm (2015)
Luque-Casado, A., Perales, J.C., Cárdenas, D., Sanabria, D.: Heart rate variability and cognitive processing: the autonomic response to task demands. Biol. Psychol. 113, 83–90 (2016)
Fan, Y., Lu, X., Li, D., & Liu, Y.: Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 445–450. ACM, October 2016
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Belda-Lois, JM. et al. (2020). Beyond Physical Domain, Understanding Workers Cognitive and Emotional Status to Enhance Worker Performance and Wellbeing. In: Ayaz, H. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-030-20473-0_4
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DOI: https://doi.org/10.1007/978-3-030-20473-0_4
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