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
Kubios hrv analysis software. https://www.kubios.com/
Airij, A.G., Bakhteri, R., Khalil-Hani, M.: Smart wearable stress monitoring device for autistic children. Jurnal Teknologi 78(7–5) (2016)
Azzaoui, N., et al.: Classifying heartrate by change detection and wavelet methods for emergency physicians. ESAIM: Proc. Surv. 45, 48–57 (2014)
Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment maninkin and semantic differential. J. Behav. Ther. Exp. Psychiatr. 25, 49–59 (1994)
Bruns, M.: Relax!: inherent feedback during product interaction to reduce stress (2010)
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)
Carr, E.G., Durand, V.M.: Reducing behavior problems through functional communication training. J. Appl. Behav. Anal. 18(2), 111–126 (1985)
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)
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)
Halder-Sinn, P., Enkelmann, C., Funsch, K.: Handwriting and emotional stress. Percept. Mot. Skills 87(2), 457–458 (1998)
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)
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)
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)
Liang, Y., Zheng, X., Zeng, D.D.: A survey on big data-driven digital phenotyping of mental health. Inf. Fus. 52, 290–307 (2019)
Marvar, P.J., et al.: T lymphocytes and vascular inflammation contribute to stress-dependent hypertension. Biol. Psychiat. 71(9), 774–782 (2012)
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)
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)
Moura, I., et al.: Mental health ubiquitous monitoring supported by social situation awareness: a systematic review. J. Biomed. Inform. 107, 103454 (2020)
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)
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)
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)
Picard, R.W.: Affective computing: challenges. Int. J. Hum Comput Stud. 59(1–2), 55–64 (2003)
Rose, D.: Enchanted objects: Design, human desire, and the Internet of things. Simon and Schuster (2014)
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)
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)
Speaks, A.: DSM-5 diagnostic criteria. Retrieved from (2014)
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)
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)
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)
Yu, B., Funk, M., Hu, J., Wang, Q., Feijs, L.: Biofeedback for everyday stress management: a systematic review. Front. ICT 5, 23 (2018)
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)
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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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