Portable Stress Detection System for Autistic Children Using Fuzzy Logic
Main Article Content
Keywords
Stress, Heart rate, Body temperature, Skin conductance
Abstract
Stress is prone to occur in children with autism. According to the study, around 85% of children who have autism suffer from anxiety disorders that can exacerbate their condition, leading to self-harm and harm to those in their vicinity. Heart rate, skin conductance, and finger temperature changes occur during stress. In this paper, we design a system to monitor heart rate, body temperature, and skin conductance to detect signs of stress. Subsequently, the measurement data is processed using the fuzzy logic (FL) method as a decision-maker algorithm. In particular, we use 64 fuzzy rules with membership functions for each parameter. Parameter measurement results will be displayed using a widget called Gauge, while stress conditions will be displayed using a label widget. The results will be displayed on the Blynk application with an IoT system and viewed remotely via Android devices. The test was conducted on five children aged 5-9 years with varying body conditions. From the test results, the mean accuracy of the heart rate sensor was 95.01%, the mean temperature sensor accuracy was 97.7%, and the mean conductance sensor accuracy was 93.75%. The stress levels range from a minimum of 25% to a maximum of 75%. These findings indicate that the developed tool has performed effectively, and it is feasible to monitor its operation remotely.
References
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[3] M. Mousikou, A. Kyriakou, and N. Skordis, “Stress and Growth in Children and Adolescents,” Horm. Res. Paediatr., vol. 96, no. 1, pp. 25–33, 2023, doi: 10.1159/000521074.
[4] S. T. Khurade, S. Gowali, C. M. C., and K. S. Shivaprakasha, “Stress Detection Indicators: A Review,” J. Electron. Commun. Syst., vol. 4, no. 1, pp. 12–17, 2019.
[5] V. Carter Leno et al., “Exposure to family stressful life events in autistic children: Longitudinal associations with mental health and the moderating role of cognitive flexibility,” Autism, vol. 26, no. 7, pp. 1656–1667, 2022, doi: 10.1177/13623613211061932.
[6] G. Makris, A. Agorastos, G. P. Chrousos, and P. Pervanidou, “Stress System Activation in Children and Adolescents With Autism Spectrum Disorder,” Front. Neurosci., vol. 15, no. January, pp. 1–15, 2022, doi: 10.3389/fnins.2021.756628.
[7] M. B. Posserud, B. Skretting Solberg, A. Engeland, J. Haavik, and K. Klungsøyr, “Male to female ratios in autism spectrum disorders by age, intellectual disability, and attention-deficit/hyperactivity disorder,” Acta Psychiatr. Scand., vol. 144, no. 6, pp. 635–646, 2021, doi: 10.1111/acps.13368.
[8] A. Puli and A. Kushki, “Toward Automatic Anxiety Detection in Autism: A Real-Time Algorithm for Detecting Physiological Arousal in the Presence of Motion,” IEEE Trans. Biomed. Eng., vol. 67, no. 3, pp. 646–657, 2020, doi: 10.1109/TBME.2019.2919273.
[9] A. M. Donnellan, M. R. Leary, and J. P. Robledo, “Stress and the Role of Movement Differences in People with Autism,” Stress Coping Autism, vol. 8, pp. 204–245, 2006, doi: 10.1093/med.
[10] A. Messina et al., “Sympathetic, metabolic adaptations, and oxidative stress in autism spectrum disorders: How far from physiology?,” Front. Physiol., vol. 9, no. MAR, pp. 1–6, 2018, doi: 10.3389/fphys.2018.00261.
[11] A. G. Airij, R. Sudirman, U. U. Sheikh, L. Y. Khuan, and N. A. Zakaria, “Significance of electrodermal activity response in children with autism spectrum disorder,” Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 2, pp. 1113–1120, 2020, doi: 10.11591/ijeecs.v19.i2.pp1113-1120.
[12] M. S. Bin, O. O. Khalifa, and R. A. Saeed, “Real-time personalized stress detection from physiological signals,” Proc. - 2015 Int. Conf. Comput. Control. Networking, Electron. Embed. Syst. Eng. ICCNEEE 2015, pp. 352–356, 2016, doi: 10.1109/ICCNEEE.2015.7381390.
[13] S. Uday, C. Jyotsna, and J. Amudha, “Detection of Stress using Wearable Sensors in IoT Platform,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2018, no. Icicct, pp. 492–498, 2018, doi: 10.1109/ICICCT.2018.8473010.
[14] S. Kambalimath and P. C. Deka, “A basic review of fuzzy logic applications in hydrology and water resources,” Appl. Water Sci., vol. 10, no. 8, pp. 1–14, 2020, doi: 10.1007/s13201-020-01276-2.
[15] J. Serrano-Guerrero, F. P. Romero, and J. A. Olivas, “Fuzzy logic applied to opinion mining: A review,” Knowledge-Based Syst., vol. 222, p. 107018, 2021, doi: 10.1016/j.knosys.2021.107018.
[16] A. Fernandes, R. Helawar, R. Lokesh, T. Tari, and A. V. Shahapurkar, “Determination of stress using Blood Pressure and Galvanic Skin Response,” 2014 Int. Conf. Commun. Netw. Technol. ICCNT 2014, vol. 2015-March, pp. 165–168, 2015, doi: 10.1109/CNT.2014.7062747.
[17] g. A. Airij, “Jurnal Teknologi SMART WEARABLE STRESS MONITORING,” vol. 5, pp. 75–81, 2016.
[18] M. E. O’Haire, S. J. Mckenzie, A. M. Beck, and V. Slaughter, “Animals may act as social buffers: Skin conductance arousal in children with autism spectrum disorder in a social context,” Dev. Psychobiol., vol. 57, no. 5, pp. 584–595, 2015, doi: 10.1002/dev.21310.
[19] M. Koussaifi et al., “Real-time Stress Evaluation using Wireless Body Sensor Networks To cite this version : HAL Id : hal-02952693 Real-time Stress Evaluation using Wireless Body Sensor Networks,” 2020.
[20] R. F. Navea, P. J. Buenvenida, and C. D. Cruz, “Stress Detection using Galvanic Skin Response: An Android Application,” J. Phys. Conf. Ser., vol. 1372, no. 1, 2019, doi: 10.1088/1742-6596/1372/1/012001.
[21] S. Betti et al., “Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers,” IEEE Trans. Biomed. Eng., vol. 65, no. 8, pp. 1748–1758, 2018, doi: 10.1109/TBME.2017.2764507.
[22] L. Rachakonda, P. Sundaravadivel, S. P. Mohanty, E. Kougianos, and M. Ganapathiraju, “A smart sensor in the IoMT for stress level detection,” Proc. - 2018 IEEE 4th Int. Symp. Smart Electron. Syst. iSES 2018, pp. 141–145, 2018, doi: 10.1109/iSES.2018.00039.
[23] E. Besic, “Implementation of first-year hardware theme project for ICT students,” no. March, 2022.
[24] K. V. S. S. Ganesh, S. P. S. Jeyanth, and A. R. Bevi, “IOT based portable heart rate and SpO2 pulse oximeter,” HardwareX, vol. 11, p. e00309, 2022, doi: 10.1016/j.ohx.2022.e00309.
[25] Ramesh Saha, S. Biswas, S. Sarmah, S. Karmakar, and P. Das, “A Working Prototype Using DS18B20 Temperature Sensor and Arduino for Health Monitoring,” SN Comput. Sci., vol. 2, no. 1, pp. 1–21, 2021, doi: 10.1007/s42979-020-00434-2.
[26] A. Whiston, E. R. Igou, D. G. Fortune, Analog Devices Team, and M. Semkovska, “Examining Stress and Residual Symptoms in Remitted and Partially Remitted Depression Using a Wearable Electrodermal Activity Device: A Pilot Study,” IEEE J. Transl. Eng. Heal. Med., vol. 11, no. July 2022, pp. 96–106, 2023, doi: 10.1109/JTEHM.2022.3228483.
[27] H. Barki and W. Y. Chung, “Mental Stress Detection Using a Wearable In-Ear Plethysmography,” Biosensors, vol. 13, no. 3, 2023, doi: 10.3390/bios13030397.
[28] S. Valenti et al., “Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements,” Biosensors, vol. 13, no. 4, 2023, doi: 10.3390/bios13040460.
[29] S. Shajari et al., “MicroSweat: A Wearable Microfluidic Patch for Noninvasive and Reliable Sweat Collection Enables Human Stress Monitoring,” Adv. Sci., vol. 10, no. 7, pp. 1–16, 2023, doi: 10.1002/advs.202204171.
[30] F. M. Talaat and R. M. El-Balka, “Stress monitoring using wearable sensors: IoT techniques in medical field,” Neural Comput. Appl., vol. 35, no. 25, pp. 18571–18584, 2023, doi: 10.1007/s00521-023-08681-z.
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Published
Jul 29, 2024
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Melinda, M., Setiawan, V., Yunidar, Y., Gopal Sakarkar, & Nurlida Basir. (2024). Portable Stress Detection System for Autistic Children Using Fuzzy Logic. Jurnal Nasional Teknik Elektro, 13(2), 60–68. https://doi.org/10.25077/jnte.v13n2.1203.2024
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