Real-time stress detection based on artificial intelligence for people with an intellectual disability

References

• Alberdi, A., Aztiria, A., & Basarab, A. (2016). Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Journal of Biomedical Informatics, 59, 49–75. https://doi.org/10.1016/j.jbi.2015.11.007
   PubMed Web of Science ®Google Scholar
• Alekhine, P. F., Rouillac, L., & Granry, J. (2020). Subjective versus objective assessment of short term occupational stress: Bias and analysis of self-assessment of high stress levels. Journal of Advances in Medicine and Medical Research, 50–64. https://doi.org/10.9734/jammr/2020/v32i1030519
   Google Scholar
• Ali, A., Blickwedel, J., & Hassiotis, A. (2014). Interventions for challenging behaviour in intellectual disability. Advances in Psychiatric Treatment, 20(3), 184–192. https://doi.org/10.1192/apt.bp.113.011577
   Google Scholar
• American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596
   Google Scholar
• Appelhans, B., & Luecken, L. (2006). Heart rate variability as an index of regulated emotional responding. Review of General Psychology, 10(3), 229–240. https://doi.org/10.1037/1089-2680.10.3.229
   Web of Science ®Google Scholar
• Bayat, A., Pomplun, M., & Tran, D. A. (2014). A study on human activity recognition using accelerometer data from smartphones. Procedia Computer Science, 34, 450–457. https://doi.org/10.1016/j.procs.2014.07.009
   Google Scholar
• Bobade, P., & Vani, M. (2020). Stress detection with machine learning and deep learning using multimodal physiological data. 2020 Second international conference on inventive research in computing applications (ICIRCA). https://doi.org/10.1109/ICIRCA48905.2020.9183244
   Google Scholar
• Boucsein, W. (2012). Methods of electrodermal recording. In W. Boucsein (Ed.), Electrodermal activity (pp. 87–258). Springer US. https://doi.org/10.1007/978-1-4614-1126-0_2
   Google Scholar
• Boucsein, W., Fowles, D., Grimnes, S., Ben-Shakhar, G., Roth, W., Dawson, M., & Filion, D. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49(8), 1017–1034. https://doi.org/10.1111/j.1469-8986.2012.01384.x
   PubMed Web of Science ®Google Scholar
• Bradley, M., & Lang, P. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49–59. https://doi.org/10.1016/0005-7916(94)90063-9
   PubMed Web of Science ®Google Scholar
• Bruinsma, E., van den Hoofdakker, B. J., Groenman, A. P., Hoekstra, P. J., de Kuijper, G. M., Klaver, M., & de Bildt, A. A. (2020). Non‐pharmacological interventions for challenging behaviours of adults with intellectual disabilities: A meta‐analysis. Journal of Intellectual Disability Research, 64(8), 561–578. https://doi.org/10.1111/jir.12736
   PubMed Web of Science ®Google Scholar
• Cacioppo, J., Tassinary, L., & Berntson, G. (2007). Handbook of psychophysiology. https://doi.org/10.1017/cbo9780511546396
   Google Scholar
• Can, Y. S., Arnrich, B., & Ersoy, C. (2019). Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. Journal of Biomedical Informatics, 92, 103139. https://doi.org/10.1016/j.jbi.2019.103139
   PubMed Web of Science ®Google Scholar
• Cappelletti, E. R., Kreuter, M. W., Boyum, S., & Thompson, T. (2015). Basic needs, stress and the effects of tailored health communication in vulnerable populations. Health Education Research, 30(4), 591–598. https://doi.org/10.1093/her/cyv033
   PubMed Web of Science ®Google Scholar
• Chebli, A., Djebbar, A., & Marouani, H. F. (2018). Semi-supervised learning for medical application: A survey. 2018 international conference on applied smart systems (ICASS), 1–9. https://doi.org/10.1109/ICASS.2018.8651980
   Google Scholar
• Cho, D., Ham, J., Oh, J., Park, J., Kim, S., Lee, N.-K., & Lee, B. (2017). Detection of stress levels from biosignals measured in virtual reality environments using a kernel-based extreme learning machine. Sensors, 17(10), 2435. https://doi.org/10.3390/s17102435
   PubMed Web of Science ®Google Scholar
• Dammen, L., van Finseth, T. T., McCurdy, B. H., Barnett, N. P., Conrady, R. A., Leach, A. G., Deick, A. F., Van Steenis, A. L., Gardner, R., Smith, B. L., Kay, A., & Shirtcliff, E. A. (2022). Evoking stress reactivity in virtual reality: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 138, 104709. https://doi.org/10.1016/j.neubiorev.2022.104709
   PubMed Web of Science ®Google Scholar
• De Vries, S., Smits, R., Tataj, M., Ronckers, M., Van Der Pol, M., Van Oost, F., Adam, E., Smaling, H., & Meinders, E. (2022). Accurate stress detection from novel real-time electrodermal activity signals and multi-task learning models. https://doi.org/10.54941/ahfe1001845
   Google Scholar
• Doodeman, T. W. M., Schuengel, C., & Sterkenburg, P. S. (2022). Expressions of stress of people with severe intellectual disabilities and sensitive caregiving to regulate stress: A qualitative study. Journal of Intellectual & Developmental Disability, 47(4), 308–317. https://doi.org/10.3109/13668250.2021.2011171
   Web of Science ®Google Scholar
• El-Hasnony, I. M., Elzeki, O. M., Alshehri, A., & Salem, H. (2022). Multi-label active learning-based machine learning model for heart disease prediction. Sensors, 22(3), 1184. https://doi.org/10.3390/s22031184
   PubMed Web of Science ®Google Scholar
• Frederiks, K., Sterkenburg, P., Barakova, E., & Feijs, L. (2019). The effects of a bioresponse system on the joint attention behaviour of adults with visual and severe or profound intellectual disabilities and their affective mutuality with their caregivers. Journal of Applied Research in Intellectual Disabilities: JARID, 32(4), 890–900. https://doi.org/10.1111/jar.12581
   PubMed Web of Science ®Google Scholar
• Gao, H., Yüce, A., & Thiran, J.-P. (2014). Detecting emotional stress from facial expressions for driving safety. 2014 IEEE International conference on image processing (ICIP), 5961–5965. https://doi.org/10.1109/ICIP.2014.7026203
   Google Scholar
• Garcia-Ceja, E., Osmani, V., & Mayora-Ibarra, O. (2015). Automatic stress detection in working environments from smartphones’ accelerometer data: A first step. IEEE Journal of Biomedical and Health Informatics, 20, 1053–1060. https://doi.org/10.1109/JBHI.2015.2446195
   PubMed Web of Science ®Google Scholar
• Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2019). Review on psychological stress detection using biosignals. https://doi.org/10.1109/TAFFC.2019.2927337
   Google Scholar
• Greco, A., Valenza, G., Lanata, A., Scilingo, E. P., & Citi, L. (2016). cvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering, 63(4), 797–804. https://doi.org/10.1109/TBME.2015.2474131
   PubMed Web of Science ®Google Scholar
• Gur, A. (2016). Challenging behavior, functioning difficulties, and quality of life of adults with intellectual disabilities. International Journal of Developmental Disabilities, 64(1), 45–52. https://doi.org/10.1080/20473869.2016.1221233
   PubMed Web of Science ®Google Scholar
• Hartikainen, S., Lipponen, J. A., Hiltunen, P., Rissanen, T. T., Kolk, I., Tarvainen, M. P., Martikainen, T. J., Castren, M., Väliaho, E.-S., & Jäntti, H. (2019). Effectiveness of the chest strap electrocardiogram to detect atrial fibrillation. The American Journal of Cardiology, 123(10), 1643–1648. https://doi.org/10.1016/j.amjcard.2019.02.028
   PubMed Web of Science ®Google Scholar
• Hartley, S. L., MacLean, W. E., & Abbeduto, L. (2009). Depression in adults with mild intellectual disability: Role of stress, attributions, and coping. American Journal on Intellectual and Developmental Disabilities, 114(3), 147–160. https://doi.org/10.1352/1944-7588-114.3.147
   PubMedGoogle Scholar
• Hong, K. (2023). Classification of emotional stress and physical stress using a multispectral based deep feature extraction model. Scientific Reports, 13(1), 2693. https://doi.org/10.1038/s41598-023-29903-3
   PubMed Web of Science ®Google Scholar
• Imbiriba, T., Cumpanasoiu, D. C., Heathers, J., Ioannidis, S., Erdoğmuş, D., & Goodwin, M. S. (2020). Biosensor prediction of aggression in youth with autism using kernel-based methods. Proceedings of the 13th ACM international conference on PErvasive technologies related to assistive environments, 1–6. https://doi.org/10.1145/3389189.3389199
   Google Scholar
• Janssen, C., Schuengel, C., & Stolk, J. (2002). Understanding challenging behaviour in people with severe and profound intellectual disability: A stress-attachment model. Journal of Intellectual Disability Research: JIDR, 46(6), 445–453. https://doi.org/10.1046/J.1365-2788.2002.00430.X
   PubMed Web of Science ®Google Scholar
• Jaques, N., Rudovic, O., Taylor, S., Sano, A., & Picard, R. W. (2017). Predicting tomorrow’s mood, health, and stress level using personalized multitask learning and domain adaptation. AffComp@IJCAI.
   Google Scholar
• Jaques, N., Taylor, S., Nosakhare, E., Sano, A., & Picard, R. W. (2016). Multi-task learning for predicting health, stress, and happiness.
   Google Scholar
• Kikhia, B., Stavropoulos, T. G., Andreadis, S., Karvonen, N., Kompatsiaris, Y., Sävenstedt, S., Pijl, M., & Melander, C. (2016). Utilizing a wristband sensor to measure the stress level for people with dementia. Sensors, 16(12), 1989. https://doi.org/10.3390/s16121989
   PubMed Web of Science ®Google Scholar
• Koolhaas, J. M., Bartolomucci, A., Buwalda, B., De Boer, S. F., Flügge, G., Korte, S. M., Meerlo, P., Murison, R., Olivier, B., Palanza, P., Richter-Levin, G., Sgoifo, A., Steimer, T., Stiedl, O., Van Dijk, G., Wöhr, M., & Fuchs, E. (2011). Stress revisited: A critical evaluation of the stress concept. Neuroscience & Biobehavioral Reviews, 35(5), 1291–1301. https://doi.org/10.1016/j.neubiorev.2011.02.003
   PubMed Web of Science ®Google Scholar
• Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F., Osborne, T., & Pykett, J. (2019). Detecting moments of stress from measurements of wearable physiological sensors. Sensors (Basel, Switzerland), 19(17), 3805. https://doi.org/10.3390/s19173805
   PubMed Web of Science ®Google Scholar
• Leborgne, F., Smits, R., Gencheva, M., De Vries, S., Meinders, E., Cluitmans, P., Lee, C., & Kuhlmann, J. (2023). The development of a washable and durable smart textile to measure electrodermal activity for early stress recognition. https://doi.org/10.54941/ahfe1002872
   Google Scholar
• Mastorakos, G., Pavlatou, M., Diamanti-Kandarakis, E., & Chrousos, G. P. (2005). Exercise and the stress system. Hormones (Athens, Greece), 4(2), 73–89. https://doi.org/10.2217/17455057.2.4.561
   PubMedGoogle Scholar
• Matthes, S., Han, Z., Qiu, T., Michel, B., Klinger, S., Shen, H., Liu, Y., & Altakrouri, B. (2020). Personalized stress detection with self-supervised learned features. https://www.semanticscholar.org/paper/Personalized-Stress-Detection-with-Self-supervised-Matthes-Han/95f19123d9fc0351e2311e93390604c81141f74b
   Google Scholar
• Nkurikiyeyezu, K., Yokokubo, A., & Lopez, G. (2019). Importance of Individual differences in physiological-based stress recognition models. 2019 15th international conference on intelligent environments (IE). https://doi.org/10.1109/IE.2019.00006
   Google Scholar
• Parak, J., Salonen, M., Myllymäki, T., & Korhonen, I. (2021). Comparison of heart rate monitoring accuracy between chest strap and vest during physical training and implications on training decisions. Sensors (Basel, Switzerland), 21(24), 8411. https://doi.org/10.3390/s21248411
   PubMed Web of Science ®Google Scholar
• Penley, J. A., Tomaka, J., & Wiebe, J. S. (2002). The association of coping to physical and psychological health outcomes: A meta-analytic review. Journal of Behavioral Medicine, 25(6), 551–603. https://doi.org/10.1023/A:1020641400589
   PubMed Web of Science ®Google Scholar
• Presland, A. D., Clare, I. C. H., Broughton, S., Luke, L., Wheeler, E., Fairchild, G., Watson, P. C., Chan, W. Y. S., Kearns, A., & Ring, H. A. (2013). Hypothalamic-pituitary-adrenal (HPA) axis activity in adults with intellectual disabilities: A preliminary investigation: HPA axis activity in adults with intellectual disabilities. Journal of Intellectual Disability Research, 57(6), 539–551. https://doi.org/10.1111/j.1365-2788.2012.01557.x
   PubMed Web of Science ®Google Scholar
• Puli, A., & Kushki, A. (2020). Toward automatic anxiety detection in autism: A real-time algorithm for detecting physiological arousal in the presence of motion. IEEE Transactions on Biomedical Engineering, 67(3), 646–657. https://doi.org/10.1109/TBME.2019.2919273
   PubMed Web of Science ®Google Scholar
• Rand, J., Hoover, A., Fishel, S., Moss, J., Pappas, J., & Muth, E. (2007). Real-time correction of heart interbeat intervals. IEEE Transactions on Biomedical Engineering, 54(5), 946–950. https://doi.org/10.1109/TBME.2007.893491
   PubMed Web of Science ®Google Scholar
• Ryan, C., Bergin, M., & Wells, J. S. G. (2021). Work-related stress and well-being of direct care workers in intellectual disability services: A scoping review of the literature. International Journal of Developmental Disabilities, 67(1), 1–22. https://doi.org/10.1080/20473869.2019.1582907
   Web of Science ®Google Scholar
• Saeed, A., Ozcelebi, T., Lukkien, J., Erp, J. V., & Trajanovski, S. (2018). Model adaptation and personalization for physiological stress detection. 2018 IEEE 5th international conference on data science and advanced analytics (DSAA), (pp. 209–216). IEEE. 10.1109/DSAA.2018.00031
   Google Scholar
• Saeed, A., & Trajanovski, S. (2017). Personalized driver stress detection with multi-task neural networks using physiological signals. ArXiv.
   Google Scholar
• Salai, M., Vassányi, I., & Kósa, I. (2016). Stress detection using low cost heart rate sensors. Journal of Healthcare Engineering, 2016, 1–13. https://doi.org/10.1155/2016/5136705
   Web of Science ®Google Scholar
• Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., & Mozos, Ó. M. (2015). Stress detection using wearable physiological sensors. International Work-Conference on the Interplay Between Natural and Artificial Computation. https://doi.org/10.1007/978-3-319-18914-7_55
   Google Scholar
• Scott, H., & Havercamp, S. (2014). Mental health for people with intellectual disability: The impact of stress and social support. American Journal on Intellectual and Developmental Disabilities, 119(6), 552–564. https://doi.org/10.1352/1944-7558-119.6.552
   PubMedGoogle Scholar
• Shaffer, F., & Ginsberg, J. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 5. https://doi.org/10.3389/fpubh.2017.00258
   PubMed Web of Science ®Google Scholar
• Siirtola, P., & Röning, J. (2020). Comparison of regression and classification models for user-independent and Personal stress detection. Sensors, 20(16), 4402. https://doi.org/10.3390/s20164402
   PubMed Web of Science ®Google Scholar
• Simons, R., Koordeman, R., De Looff, P., & Otten, R. (2021). Physiological measurements of stress preceding incidents of challenging behavior in people with severe to profound intellectual disabilities: Longitudinal study protocol of single-case studies. JMIR Research Protocols, 10(7), e24911. https://doi.org/10.2196/24911
   PubMed Web of Science ®Google Scholar
• Smith, M., Manduchi, B., Burke, É., Carroll, R., McCallion, P., & McCarron, M. (2020). Communication difficulties in adults with intellectual disability: Results from a national cross-sectional study. Research in Developmental Disabilities, 97, 103557. https://doi.org/10.1016/j.ridd.2019.103557
   PubMed Web of Science ®Google Scholar
• Steptoe, A., & Kivimäki, M. (2012). Stress and cardiovascular disease. Nature Reviews Cardiology, 9(6), 360–370. https://doi.org/10.1038/nrcardio.2012.45
   PubMed Web of Science ®Google Scholar
• Stroop, J. (1992). Studies of interference in serial verbal reactions. Journal of Experimental Psychology: General, 18(6), 643–662. https://doi.org/10.1037/h0054651
   Google Scholar
• Taylor, S., Jaques, N., Nosakhare, E., Sano, A., & Picard, R. W. (2020). Personalized multitask learning for predicting tomorrow’s mood, stress, and health. IEEE transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2017.2784832
   Google Scholar
• Tomczak, M. T., Wojcikowski, M., Pankiewicz, B., Lubinski, J., Majchrowicz, J., Majchrowicz, D., Walasiewicz, A., Kilinski, T., & Szczerska, M. (2020). Stress monitoring system for individuals with autism spectrum disorders. IEEE Access, 8, 228236–228244. https://doi.org/10.1109/ACCESS.2020.3045633
   Web of Science ®Google Scholar
• Vildjiounaite, E., Kallio, J., Kyllönen, V., Nieminen, M., Määttänen, I., Lindholm, M., Mäntyjärvi, J., & Gimel’farb, G. (2018). Unobtrusive stress detection on the basis of smartphone usage data. Personal and Ubiquitous Computing, 22(4), 671–688. https://doi.org/10.1007/s00779-017-1108-z
   Web of Science ®Google Scholar
• Vos, P., De Cock, P., Munde, V., Petry, K., Van Den Noortgate, W., & Maes, B. (2012). The tell-tale: What do heart rate; skin temperature and skin conductance reveal about emotions of people with severe and profound intellectual disabilities? Research in Developmental Disabilities, 33(4), 1117–1127. https://doi.org/10.1016/j.ridd.2012.02.006
   PubMed Web of Science ®Google Scholar
• Wu, M., Cao, H., Nguyen, H.-L., Surmacz, K., & Hargrove, C. (2015). Modeling perceived stress via HRV and accelerometer sensor streams. 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). https://doi.org/10.1109/EMBC.2015.7318686
   Google Scholar
• Yaribeygi, H., Panahi, Y., Sahraei, H., Johnston, T. P., & Sahebkar, A. (2017). The impact of stress on body function: A review. Excli Journal 16, 1057–1072. Doc1057; ISSN 1611-2156. https://doi.org/10.17179/EXCLI2017-480
   PubMed Web of Science ®Google Scholar
• ZIN. (2019). Screeningsrapport Gehandicaptenzorg Zinnige Zorg.
   Google Scholar