References
- Acharya, U. R., S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli. 2018. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine 100 (9):270–78. doi:https://doi.org/10.1016/j.compbiomed.2017.09.017.
- Agrafioti, F., D. Hatzinakos, and A. K. Anderson. 2012. ECG pattern analysis for emotion detection. IEEE Transactions on Affective Computing 3 (1):102–15. doi:https://doi.org/10.1521/jaap.1.1976.4.3.411.
- Alhagry, S., A. A. Fahmy, and R. A. El-Khoribi. 2017. Emotion recognition based on EEG using LSTM recurrent neural network. International Journal of Advanced Computer Science and Applications 8 (10):8–11. doi:https://doi.org/10.14569/ijacsa.2017.081046.
- Alzubi, J., A. Nayyar, and A. Kumar. 2018. Machine learning from theory to algorithms: An overview. Journal of Physics: Conference Series 1142 (1). doi: https://doi.org/10.1088/1742-6596/1142/1/012012.
- Anzalone, S. M., E. Tilmont, S. Boucenna, J. Xavier, A.-L. Jouen, N. Bodeau, … D. Cohen. 2014. How children with autism spectrum disorder behave and explore the 4-dimensional (spatial 3D + time) environment during a joint attention induction task with a robot. Research in Autism Spectrum Disorders 8:814–26. doi:https://doi.org/10.1016/j.rasd.2014.03.002.
- Apple. 2019. Accessed March 7, 2019. https://www.apple.com/ie/apple-watch-series-4/.
- Awake Labs. 2019. https://awakelabs.com/news-clinical-trial-community-pilot/.
- Baker, S. B., W. E. I. Xiang, S. Member, and I. A. N. Atkinson. 2017. Internet of things for smart healthcare : Technologies, challenges, and opportunities. IEEE Access 5:26521–44. doi:https://doi.org/10.1109/ACCESS.2017.2775180.
- Bessette Gorlin, J., C. P. McAlpine, A. Garwick, and E. Wieling. 2016. Severe childhood Autism: The family lived experience. Journal of Pediatric Nursing 31 (6):580–97. doi:https://doi.org/10.1016/j.pedn.2016.09.002.
- Cabibihan, J. J., R. Chellali, C. W. C. So, M. Aldosari, O. Connor, A. Y. Alhaddad, and H. Javed. 2018. Social robots and wearable sensors for mitigating meltdowns in autism - A pilot test. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11357 LNAI. Qingdao, China: Springer International Publishing. doi:https://doi.org/10.1007/978-3-030-05204-1_11.
- Cheng, B., and G. Liu. 2008. Emotion recognition from surface EMG signal using wavelet transform and neural network. 2nd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2008, Vol. 1, Shanghai, China, 1363–66. doi:https://doi.org/10.1109/ICBBE.2008.670.
- Cheng, Y., S. Y. Luo, H. C. Lin, and C. S. Yang. 2018. Investigating mobile emotional learning for children with autistic spectrum disorders. International Journal of Developmental Disabilities 64 (1):25–34. doi:https://doi.org/10.1080/20473869.2016.1206729.
- Critchley, H. D. 2002. Review: Electrodermal responses: What happens in the brain. The Neuroscientist 8 (2):132–42. doi:https://doi.org/10.1177/107385840200800209.
- Empatica. 2019. https://store.empatica.com/products/e4-wristband?variant=39588207747.
- Empatica Inc. 2019. https://www.empatica.com/embrace2/.
- Fitzpatrick, S. E., L. Srivorakiat, L. K. Wink, E. V. Pedapati, and C. A. Erickson. 2016. Aggression in autism spectrum disorder: Presentation and treatment options. Neuropsychiatric Disease and Treatment 12:1525–38. doi:https://doi.org/10.2147/NDT.S84585.
- Goodwin, M. S., J. Groden, W. F. Velicer, L. P. Lipsitt, M. G. Baron, S. G. Hofmann, and G. Groden. 2006. Cardiovascular arousal in individuals with Autism. Focus on Autism and Other Developmental Disabilities 21 (2):100–23. doi:https://doi.org/10.1177/10883576060210020101.
- Guendil, Z., Z. Lachiri, C. Maaoui, and A. Pruski 2015. Emotion recognition from physiological signals using fusion of wavelet based features. 2015 7th International Conference on Modelling, Identification and Control (ICMIC), 1–6. Sousse, Tunisia: IEEE. doi:https://doi.org/10.1109/ICMIC.2015.7409485.
- Happé, F. 2018. Why are savant skills and special talents associated with autism? World Psychiatry 17 (3):280–81. doi:https://doi.org/10.1002/wps.20552.
- Hernando, D., S. Roca, J. Sancho, Á. Alesanco, and R. Bailón. 2018. Validation of the apple watch for heart rate variability measurements during relax and mental stress in healthy subjects. Sensors 18 (8):2619. doi:https://doi.org/10.3390/s18082619.
- Hirstein, W., P. Iversen, and V. S. Ramachandran. 2001. Autonomic responses of autistic children to people and objects. Proceedings of the Royal Society B: Biological Sciences 268 (1479):1883–88. doi:https://doi.org/10.1098/rspb.2001.1724.
- Hu, P., S. Dhelim, H. Ning, and T. Qiu. 2017. Survey on fog computing: Architecture, key technologies, applications and open issues. Journal of Network and Computer Applications, Accepted. doi:https://doi.org/10.1016/j.jnca.2017.09.002.
- Hufnagel, C., P. Chambres, P. R. Bertrand, and F. Dutheil. 2017. The need for objective measures of stress in Autism. Frontiers in Psychology 8 (January):8–11. doi:https://doi.org/10.3389/fpsyg.2017.00064.
- Is QardioCore Clinically Validated. 2019. https://support.getqardio.com/hc/en-.
- Koo, S. H., K. Gaul, S. Rivera, T. Pan, and D. Fong. 2018. Wearable technology design for Autism spectrum disorders. Archives of Design Research 31 (1):37–55. doi:https://doi.org/10.15187/adr.2018.02.31.1.37.
- Krupa, N., K. Anantharam, M. Sanker, S. Datta, and J. V. Sagar. 2016. Recognition of emotions in autistic children using physiological signals. Health and Technology 6 (2):137–47. doi:https://doi.org/10.1007/s12553-016-0129-3.
- Lan, Z., O. Sourina, L. Wang, and Y. Liu. 2016. Real-time EEG-based emotion monitoring using stable features. Visual Computer 32 (3):347–58. doi:https://doi.org/10.1007/s00371-015-1183-y.
- Lecun, Y., Y. Bengio, and G. Hinton. 2015 May 27. Deep learning. Nature. doi:https://doi.org/10.1038/nature14539.
- Lin, Y. P., C. H. Wang, T. P. Jung, T. L. Wu, S. K. Jeng, J. R. Duann, and J. H. Chen. 2010. EEG-based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering 57 (7):1798–806. doi:https://doi.org/10.1109/TBME.2010.2048568.
- Litjens, G., T. Kooi, B. E. Bejnordi, A. Arindra, A. Setio, F. Ciompi, … C. I. Sánchez. 2017. A survey on deep learning in medical image analysis. Medical Image Analysis Journal 42:60–88. doi:https://doi.org/10.1016/j.media.2017.07.005.
- Luo, E., M. Z. A. Bhuiyan, G. Wang, M. A. Rahman, J. Wu, and M. Atiquzzaman. 2018. PrivacyProtector: Privacy-protected patient data collection in IoT-based healthcare systems. IEEE Communications Magazine 56 (2):163–68. doi:https://doi.org/10.1109/MCOM.2018.1700364.
- Lydon, S., O. Healy, and M. Dwyer. 2013. An examination of heart rate during challenging behavior in Autism spectrum disorder. Journal of Developmental and Physical Disabilities 25 (1):149–70. doi:https://doi.org/10.1007/s10882-012-9324-y.
- McGinnis, R. S., E. W. McGinnis, J. Hruschak, N. L. Lopez-Duran, K. Fitzgerald, K. L. Rosenblum, and M. Muzik. 2019. Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning. PLoS ONE 14 (1):1–16. doi:https://doi.org/10.1371/journal.pone.0210267.
- Mirmohamadsadeghi, L., A. Yazdani, and J.-M. Vesin 2016. Using cardio-respiratory signals to recognize emotions elicited by watching music video clips. 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Vol.1, 1–5. Montreal, Canada: IEEE. doi:https://doi.org/10.1109/MMSP.2016.7813349.
- Monajati, M., S. H. Abbasi, F. Shabaninia, and S. Shamekhi. 2012. Emotions states recognition based on physiological parameters by employing of fuzzy-adaptive resonance theory. International Journal of Intelligence Science 2 (4):166–75. doi:https://doi.org/10.4236/ijis.2012.224022.
- Montaque, I., R. Dallos, and B. McKenzie. 2018. It feels like something difficult is coming back to haunt me: An exploration of ‘meltdowns’ associated with autistic spectrum disorder from a parental perspective. Clinical Child Psychology and Psychiatry 23 (1):125–39. doi:https://doi.org/10.1177/1359104517730114.
- Nayyar, A., V. Puri, and N. G. Nguyen (2019). BioSenHealth 1.0: A novel internet of medical things (IoMT)-based patient health monitoring system. In S. Bhattacharyya, A. E. Hassanien, D. Gupta, A. Khanna, and I. Pan (Eds.), International Conference on Innovative Computing and Communications, Vol.55, 155–64. Singapore: Springer Singapore. doi:https://doi.org/10.1007/978-981-13-2324-9_16.
- Northrup, C. M., J. Lantz, T. Hamlin, and C. M. Northrup. 2016. Wearable stress sensors for children with Autism spectrum disorder with in situ alerts to caregivers via a mobile phone corresponding author. Iproceedings 2 (1):9–10. doi:https://doi.org/10.2196/iproc.6119.
- Oh, S. L., E. Y. K. Ng, R. S. Tan, and U. R. Acharya. 2018. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in Biology and Medicine 102:278–87. doi:https://doi.org/10.1016/j.compbiomed.2018.06.002.
- Panju, S., J. Brian, A. Dupuis, E. Anagnostou, and A. Kushki. 2015. Atypical sympathetic arousal in children with autism spectrum disorder and its association with anxiety symptomatology. Molecular Autism 1–10. doi:https://doi.org/10.1186/s13229-015-0057-5.
- Qardio. 2019. https://www.getqardio.com/qardiocore-wearable-ecg-ekg-monitor-iphone/.
- Ryan, S. 2010. “Meltdowns,” surveillance and managing emotions; going out with children with autism. Health & Place 16 (5):868–75. doi:https://doi.org/10.1016/j.healthplace.2010.04.012.
- Samara, A., M. L. R. Menezes, and L. Galway 2016. Feature extraction for emotion recognition and modelling using neurophysiological data. 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS), 138–44. Granada, Spain: IEEE. doi:https://doi.org/10.1109/IUCC-CSS.2016.027.
- Sasikumar, K., C. George Priya Doss, and K. Adalarasu. 2015. Analysis of physiological signal variation between autism and control group in south indian population. Biomedical Research (India) 26 (3):525–29.
- Sentio Solutions. 2019. Accessed March 7, 2019. https://www.myfeel.co/reserve.
- Sigman, M., C. Dissanayake, R. Corona, and M. Espinosa. 2003. Social and cardiac responses of young children with Autism. Autism 7 (2):205–16. doi:https://doi.org/10.1177/1362361303007002007.
- Simm, W., M. A. Ferrario, A. Gradinar, M. T. Smith, S. Forshaw, I. Smith, and J. Whittle (2016). Anxiety and Autism : Towards personalized digital health. CHI Conference on Human Factors in Computing Systems, 1–12. San Jose, CA. doi:https://doi.org/10.1145/2858036.2858259.
- Tahsien, S. M., H. Karimipour, and P. Spachos. 2020. Machine learning based solutions for security of internet of things (IoT): A survey. Journal of Network and Computer Applications 161 (April). doi: https://doi.org/10.1016/j.jnca.2020.102630.
- ViSi Mobile. n.d. https://www.visimobile.com/visi-mobile/.
- Weenk, M., H. van Goor, B. Frietman, L. J. Engelen, C. J. van Laarhoven, J. Smit, … T. H. Van De Belt. 2017. Continuous monitoring of vital signs using wearable devices on the general ward: Pilot study. JMIR MHealth and UHealth 5 (7):e91. doi:https://doi.org/10.2196/mhealth.7208.
- Welch, K. C. 2012. Physiological signals of autistic children can be useful. IEEE Instrumentation & Measurement Magazine 15 (1):28–32. doi:https://doi.org/10.1109/MIM.2012.6145259.
- Yu, G., X. Li, D. Song, X. Zhao, P. Zhang, Y. Hou, and B. Hu (2016). Encoding physiological signals as images for affective state recognition using convolutional neural networks. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vol. 2016-octob, 812–15. Florida, USA: IEEE. doi:https://doi.org/10.1109/EMBC.2016.7590825.
- Yugha, R., and S. Chithra. 2020. A survey on technologies and security protocols : Reference for future generation IoT. Journal of Network and Computer Applications 169 (September):2019. doi:https://doi.org/10.1016/j.jnca.2020.102763.
- Zhao, S., A. Gholaminejad, G. Ding, Y. Gao, J. Han, and K. Keutzer. 2019. Personalized emotion recognition by personality-aware high-order learning of physiological signals. ACM Transactions on Multimedia Computing, Communications, and Applications 15 (1s):1–18. doi:https://doi.org/10.1145/3233184.