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ApEn: A Stress-Aware Pen for Children with Autism Spectrum Disorder

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Abstract

Children with Autism Spectrum disorder (ASD) often experience high levels of anxiety and stress. Many children with ASD have difficulty in being aware of their stress and communicating distress to family and caregivers. Stress detection and regulation are vital for their mental well-being. This paper presents a stress-aware pen (ApEn) that detects real-time stress-related behaviors and interacts with users with vibrotactile and light as a feedback indication of interpreted stress levels. ApEn is a context-aware tool for collecting behavioral data related to the expression of stress and can increase users’ stress awareness. A pilot test was conducted with typical developed children to investigate how to detect stress in their daily environment. The pilot test results indicate that ApEn is a promising tool for detecting stress-related behaviors and can attend the user about the detected stress through designed sensory feedback.

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References

  1. Kubios hrv analysis software. https://www.kubios.com/

  2. Airij, A.G., Bakhteri, R., Khalil-Hani, M.: Smart wearable stress monitoring device for autistic children. Jurnal Teknologi 78(7–5) (2016)

    Google Scholar 

  3. Azzaoui, N., et al.: Classifying heartrate by change detection and wavelet methods for emergency physicians. ESAIM: Proc. Surv. 45, 48–57 (2014)

    Google Scholar 

  4. Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment maninkin and semantic differential. J. Behav. Ther. Exp. Psychiatr. 25, 49–59 (1994)

    Google Scholar 

  5. Bruns, M.: Relax!: inherent feedback during product interaction to reduce stress (2010)

    Google Scholar 

  6. Can, Y.S., Arnrich, B., Ersoy, C.: Stress detection in daily life scenarios using smart phones and wearable sensors: a survey. J. Biomed. Inform. 92, 103139 (2019)

    Google Scholar 

  7. Carr, E.G., Durand, V.M.: Reducing behavior problems through functional communication training. J. Appl. Behav. Anal. 18(2), 111–126 (1985)

    Article  CAS  Google Scholar 

  8. Fleege, P.O., Charlesworth, R., Burts, D.C., Hart, C.H.: Stress begins in kindergarten: a look at behavior during standardized testing. J. Res. Child. Educ. 7(1), 20–26 (1992)

    Google Scholar 

  9. Gjoreski, M., Gjoreski, H., Luštrek, M., Gams, M.: Continuous stress detection using a wrist device: in laboratory and real life. In: proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 1185–1193 (2016)

    Google Scholar 

  10. Halder-Sinn, P., Enkelmann, C., Funsch, K.: Handwriting and emotional stress. Percept. Mot. Skills 87(2), 457–458 (1998)

    Google Scholar 

  11. Keinan, G., Eilat-Greenberg, S.: Can stress be measured by handwriting analysis? The effectiveness of the analytic method. Appl. Psychol. Int. Rev. 42(2), 153–170 (1993)

    Google Scholar 

  12. Koo, S.H., Gaul, K., Rivera, S., Pan, T., Fong, D.: Wearable technology design for autism spectrum disorders. Arch. Des. Res. 31(1), 37–55 (2018)

    Google Scholar 

  13. Koskinen, I., Zimmerman, J., Binder, T., Redstrom, J., Wensveen, S.: Design research through practice: from the lab, field, and showroom. IEEE Trans. Prof. Commun. 56(3), 262–263 (2013)

    Google Scholar 

  14. Liang, Y., Zheng, X., Zeng, D.D.: A survey on big data-driven digital phenotyping of mental health. Inf. Fus. 52, 290–307 (2019)

    Google Scholar 

  15. Marvar, P.J., et al.: T lymphocytes and vascular inflammation contribute to stress-dependent hypertension. Biol. Psychiat. 71(9), 774–782 (2012)

    Google Scholar 

  16. Merikangas, K.R., et al.: Lifetime prevalence of mental disorders in us adolescents: results from the national comorbidity survey replication-adolescent supplement (NCS-A). J. Am. Acad. Child Adolescent Psychiatr. 49(10), 980–989 (2010)

    Google Scholar 

  17. Mohr, D.C., Zhang, M., Schueller, S.M.: Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu. Rev. Clin. Psychol. 13, 23–47 (2017)

    Google Scholar 

  18. Moura, I., et al.: Mental health ubiquitous monitoring supported by social situation awareness: a systematic review. J. Biomed. Inform. 107, 103454 (2020)

    Google Scholar 

  19. de Moura, I.R., Teles, A.S., Endler, M., Coutinho, L.R., da Silva E Silva, F.J.: Recognizing context-aware human sociability patterns using pervasive monitoring for supporting mental health professionals. Sensors 21(1), 86 (2021)

    Google Scholar 

  20. Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(1), 1–12 (2009)

    Google Scholar 

  21. Parlak, O., Keene, S.T., Marais, A., Curto, V.F., Salleo, A.: Molecularly selective nanoporous membrane-based wearable organic electrochemical device for noninvasive cortisol sensing. Sci. Adv. 4(7), eaar2904 (2018)

    Google Scholar 

  22. Picard, R.W.: Affective computing: challenges. Int. J. Hum Comput Stud. 59(1–2), 55–64 (2003)

    Google Scholar 

  23. Rose, D.: Enchanted objects: Design, human desire, and the Internet of things. Simon and Schuster (2014)

    Google Scholar 

  24. Seifi, H., Zhang, K., MacLean, K.E.: VibViz: organizing, visualizing and navigating vibration libraries. In: 2015 IEEE World Haptics Conference (WHC), pp. 254–259. IEEE (2015)

    Google Scholar 

  25. Sharawi, M.S., Shibli, M., Sharawi, M.I.: Design and implementation of a human stress detection system: a biomechanics approach. In: 2008 5th International Symposium on Mechatronics and Its Applications, pp. 1–5. IEEE (2008)

    Google Scholar 

  26. Speaks, A.: DSM-5 diagnostic criteria. Retrieved from (2014)

    Google Scholar 

  27. Ståhl, A., Jonsson, M., Mercurio, J., Karlsson, A., Höök, K., Johnson, E.C.B.: The soma mat and breathing light. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 305–308 (2016)

    Google Scholar 

  28. Taj-Eldin, M., Ryan, C., O’Flynn, B., Galvin, P.: A review of wearable solutions for physiological and emotional monitoring for use by people with autism spectrum disorder and their caregivers. Sensors 18(12), 4271 (2018)

    Google Scholar 

  29. Tonhajzerova, I., Mestanik, M., Mestanikova, A., Jurko, A.: Respiratory sinus arrhythmia as a non-invasive index of ‘brain-heart’ interaction in stress. Indian J. Med. Res. 144(6), 815 (2016)

    Google Scholar 

  30. Yu, B., Funk, M., Hu, J., Wang, Q., Feijs, L.: Biofeedback for everyday stress management: a systematic review. Front. ICT 5, 23 (2018)

    Google Scholar 

  31. Yu, B., Hu, J., Funk, M., Feijs, L.: Delight: biofeedback through ambient light for stress intervention and relaxation assistance. Pers. Ubiquit. Comput. 22(4), 787–805 (2018)

    Google Scholar 

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Correspondence to Jing Li .

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Li, J., Barakova, E., Hu, J., Staal, W., van Dongen-Boomsma, M. (2022). ApEn: A Stress-Aware Pen for Children with Autism Spectrum Disorder. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_28

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  • Print ISBN: 978-3-031-06241-4

  • Online ISBN: 978-3-031-06242-1

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