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Detecting Emotions During Cognitive Stimulation Training with the Pepper Robot

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Human-Friendly Robotics 2021

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 23))

Abstract

Recently, social robots are being used in therapeutic interventions for elderly people affected by cognitive impairments. In this paper, we report the results of a study aiming at exploring the affective reactions of seniors during the cognitive stimulation therapy performed using a social robot. To this purpose an experimental study was performed with a group of 8 participants in a 3-weeks program in which the group was trained on specific memory tasks with the support of the Pepper robot. To assess and monitor the results, each session was video-recorded for human and automatic analyses. Given that aging causes many changes in facial shape and appearance, we detected emotions by means of a model specifically trained for recognizing facial expressions of elderly people. After testing the model accuracy and analyzing the differences with the human annotation, we used it to analyze automatically the collected videos. Results show that the model was able to detect a low number of neutral emotions and a high number of negative emotions. However, seniors showed also positive emotions during the various sessions and, while these were much higher than negative ones in the human annotation, this difference was smaller in the automatic detection. These results encourage the development of a module to adapt the interaction and the tasks to the user’s reactions in real time. In both cases, some correlations emerged showing that seniors with a lower level of cognitive impairment experienced fewer positive emotions than seniors with a more severe impairment measured with the Mini–Mental State Examination (MMSE). In our opinion, this could be due to the need for personalized cognitive stimulation therapy according to the senior’s MMSE thus providing more stimulating tasks. However, a deeper investigation should be conducted to confirm this hypothesis.

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References

  1. Cooper, C., Mukadam, N., Katona, C., Lyketsos, C.G., Ames, D., Rabins, P., Engedal, K., de Mendonça Lima, C., Blazer, D., Teri, L., et al.: Systematic review of the effectiveness of non-pharmacological interventions to improve quality of life of people with dementia. In: Database of Abstracts of Reviews of Effects (DARE): Quality-assessed Reviews [Internet]. Centre for Reviews and Dissemination (UK) (2012)

    Google Scholar 

  2. Pino, O.: Memory impairments and rehabilitation: evidence-based effects of approaches and training programs. Open Rehabil. J. 8(1), 25–33 (2015). https://doi.org/10.2174/1874943720150601E001

    Article  Google Scholar 

  3. Rouaix, N., Retru-Chavastel, L., Rigaud, A.-S., Monnet, C., Lenoir, H., Pino, M.: Affective and engagement issues in the conception and assessment of a robot-assisted psycho-motor therapy for persons with dementia. Front. Psychol. 8, 950 (2017)

    Article  Google Scholar 

  4. Law, M., Sutherland, C., Ahn, H.S.,, MacDonald, B.A., Peri, K., Johanson, D.L., Vajsakovic, D.-S., Kerse, N., Broadbent, E.: Developing assistive robots for people with mild cognitive impairment and mild dementia: a qualitative study with older adults and experts in aged care. BMJ Open 9(9), e031937 (2019)

    Google Scholar 

  5. Pino, O., Palestra, G., Trevino, R., De Carolis, B.: The humanoid robot nao as trainer in a memory program for elderly people with mild cognitive impairment. Int. J. Soc. Robot. 12(1), 21–33 (2020)

    Article  Google Scholar 

  6. Valenti-Soler, et al.: Social robots in advanced dementia. Front. Aging Neurosci. 7, 5 (2015)

    Google Scholar 

  7. Manca, M., Paterno, F., Santoro, C., Zedda, E., Braschi, C., Franco, R., Sale, A.: The impact of serious games with humanoid robots on mild cognitive impairment older adults. Int. J. Hum. Comput. Stud. 102509 (2020)

    Google Scholar 

  8. https://www.softbankrobotics.com/emea/en/pepper

  9. De Carolis, B., Carofiglio, V., Grimaldi, I., Macchiarulo, N., Palestra, G., Pino, O.: Using the pepper robot in cognitive stimulation therapy for people with mild cognitive impairment and mild dementia. In: ACHI 2020, The Thirteenth International Conference on Advances in Computer-Human Interactions, Valencia, Spain, 21–25 November 2020

    Google Scholar 

  10. Ebner, N.C., Johnson, M.K.: Age-group differences in interference from young and older emotional faces. Cogn. Emot. 24(7), 1095–1116 (2010)

    Article  Google Scholar 

  11. Kasper, S., Bancher, C., Eckert, A., Förstl, H., Frölich, L., Hort, J., Korczyn, A.D., Kressig, R.W., Levin, O., Paloma, M.S.M.: Management of Mild Cognitive Impairment (MCI): the need for national and international guidelines. World J. Biol. Psychiatry 21(8), 579–594 (2020)

    Article  Google Scholar 

  12. Kim, G.H., Jeon, S., Im, K., Kwon, H., Lee, B.H., Kim, G.Y., Jeong, H., Han, N.E., Seo, S.W., Cho, H., et al.: Structural brain changes after traditional and robot-assisted multi-domain cognitive training in community-dwelling healthy elderly. PLoS One 10(4), e0123251 (2015)

    Google Scholar 

  13. Mataric, M.J., Scassellati, B.: Socially assistive robotics. In: Springer Handbook of Robotics, pp. 1973–1994. Springer (2016)

    Google Scholar 

  14. Vogan, A.A., Alnajjar, F., Gochoo, M., Khalid, S.: Robots, AI, and cognitive training in an era of mass age-related cognitive decline: a systematic review. IEEE Access 8, 18284–18304 (2020)

    Google Scholar 

  15. Martín, F., Agüero, C.E., Cañas, J.M., Valenti, M., Martínez-Martín, P.: Robotherapy with Dementia patients. Int. J. Adv. Rob. Syst. (2013). https://doi.org/10.5772/54765

    Article  Google Scholar 

  16. De Kok, R., Rothweiler, J., Scholten, L., van Zoest, M., Boumans, R., Neerincx, M.: Combining social robotics and music as a non-medical treatment for people with dementia. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 465–467 (2018)

    Google Scholar 

  17. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  18. Esme, B., Sankur, B.: Effects of aging over facial feature analysis and face recognition (2010)

    Google Scholar 

  19. Ebner, N., Riediger, M., Lindenberger, U.: Faces: a database of facial expressions in young, middle-aged, and older women and men: development and validation. Behav. Res. Methods 42, 351–62 (2010)

    Google Scholar 

  20. Guo, G., Guo, R.-X., Li, X.: Facial expression recognition influenced by human aging. IEEE Trans. Affect. Comput. 4, 291–298 (2013)

    Google Scholar 

  21. Caroppo, A., Leone, A., Siciliano, P.: Facial expression recognition in older adults using deep machine learning. AI*AAL@AI*IA (2017)

    Google Scholar 

  22. Lopes, N., et al.: Facial emotion recognition in the elderly using a SVM classifier. In: 2018 2nd International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW), pp. 1–5 (2018). https://doi.org/10.1109/TISHW.2018.8559494

  23. Folstein, M.F., Folstein, S.E., McHugh, P.R.: “mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12(3), 189–198 (1975)

    Article  Google Scholar 

  24. Fleiss, J., Cohen, J.: The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ. Psychol. Measur. 33(1973): 613–619

    Google Scholar 

  25. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  26. Ekman, P., Friesen, W.: Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto (1978)

    Google Scholar 

  27. OpenFace 2.0: Facial behavior analysis toolkit. In: Baltrušaitis, T., Zadeh, A., Lim, Y.C., Morency, L.-P.: IEEE International Conference on Automatic Face and Gesture Recognition (2018)

    Google Scholar 

  28. Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  29. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  30. Weston, J., Watkins, C.: Multi-class support vector machine (1999)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the “Alzheimer Bari” ONLUS, the two therapists Claudia Lograno and Claudia Chiapparino for their support, and all the seniors who participated to the experiment.

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Correspondence to Nicola Macchiarulo .

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Castellano, G., De Carolis, B., Macchiarulo, N., Pino, O. (2022). Detecting Emotions During Cognitive Stimulation Training with the Pepper Robot. In: Palli, G., Melchiorri, C., Meattini, R. (eds) Human-Friendly Robotics 2021. Springer Proceedings in Advanced Robotics, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-96359-0_5

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