Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli

The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to...

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Autores:: Sanchez-Comas, Andres
Synnes, Kåre
Molina Estren, Diego
Troncoso Palacio, Alexander
Comas Gonzalez, Zhoe

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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repository_id_str
dc.title.spa.fl_str_mv	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli
title	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli
spellingShingle	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli stress wearable sensor physiological signals galvanic skin response GSR electrodermal activity EDA pleasant and unpleasant stimuli valence correlation
title_short	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli
title_full	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli
title_fullStr	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli
title_full_unstemmed	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli
title_sort	Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli
dc.creator.fl_str_mv	Sanchez-Comas, Andres Synnes, Kåre Molina Estren, Diego Troncoso Palacio, Alexander Comas Gonzalez, Zhoe
dc.contributor.author.spa.fl_str_mv	Sanchez-Comas, Andres Synnes, Kåre Molina Estren, Diego Troncoso Palacio, Alexander Comas Gonzalez, Zhoe
dc.subject.spa.fl_str_mv	stress wearable sensor physiological signals galvanic skin response GSR electrodermal activity EDA pleasant and unpleasant stimuli valence correlation
topic	stress wearable sensor physiological signals galvanic skin response GSR electrodermal activity EDA pleasant and unpleasant stimuli valence correlation
description	The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-06-24T18:09:09Z
dc.date.available.none.fl_str_mv	2021-06-24T18:09:09Z
dc.date.issued.none.fl_str_mv	2021-06-19
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.uri.spa.fl_str_mv	https://hdl.handle.net/11323/8410
dc.identifier.doi.spa.fl_str_mv	https://doi.org/10.3390/s21124210
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
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url	https://hdl.handle.net/11323/8410 https://doi.org/10.3390/s21124210 https://repositorio.cuc.edu.co/
identifier_str_mv	Corporación Universidad de la Costa REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Kumari, P.; Mathew, L.; Syal, P. Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens. Bioelectron. 2017, 90, 298–307. Ni, Q.; Hernando, A.B.G.; de la Cruz, I.P. The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development. Sensors 2015, 15, 11312–11362. [ Peetoom, K.K.B.; Lexis, M.A.S.; Joore, M.; Dirksen, C.D.; de Witte, L.P. Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil. Rehabil. Assist. Technol. 2015, 10, 271–294. Jekel, K.; Damian, M.; Storf, H.; Hausner, L.; Frolich, L. Development of a Proxy-Free Objective Assessment Tool of Instrumental Activities of Daily Living in Mild Cognitive Impairment Using Smart Home Technologies. J. Alzheimer Dis. 2016, 52, 509–517. Coronato, A.; de Pietro, G.; Paragliola, G. A situation-aware system for the detection of motion disorders of patients with autism spectrum disorders. Expert Syst. Appl. 2014, 41, 7868–7877. Vanus, J.; Belesova, J.; Martinek, R.; Nedoma, J.; Fajkus, M.; Bilik, P.; Zidek, J. Monitoring of the daily living activities in smart home care. Hum. Cent. Comput. Inf. Sci. 2017, 7, 1–34. Sanchez-Comas, A.; Synnes, K.; Hallberg, J. Hardware for recognition of human activities: A review of smart home and AAL related technologies. Sensors 2020, 20, 4427. Fernández-Caballero, A.; Martínez-Rodrigo, A.; Pastor, J.M.; Castillo, J.C.; Lozano-Monasor, E.; López, M.T.; Zangróniz, R.; Latorre, J.M.; Fernández-Sotos, A. Smart environment architecture for emotion detection and regulation. J. Biomed. Inform. 2016, 64, 55–73. Mendoza-Palechor, F.; Menezes, M.L.; Sant’Anna, A.; Ortiz-Barrios, M.; Samara, A.; Galway, L. Affective recognition from EEG signals: An integrated data-mining approach. J. Ambient Intell. Humaniz. Comput. 2019, 10, 3955–3974. Menezes, M.L.R.; Samara, A.; Galway, L.; Sant’Anna, A.; Alonso-Fernandez, F.; Wang, H.; Bond, R. Towards emotion recognition for virtual environments: An evaluation of eeg features on benchmark dataset. Pers. Ubiquitous Comput. 2017, 21, 1003–1013. Raheel, A.; Majid, M.; Alnowami, M.; Anwar, S.M. Physiological sensors based emotion recognition while experiencing tactile enhanced multimedia. Sensors 2020, 20, 4037. Kang, J.; Larkin, H. Application of an Emergency Alarm System for Physiological Sensors Utilizing Smart Devices. Technologies 2017, 5, 26. Zhang, Z.; Song, Y.; Cui, L.; Liu, X. Emotion recognition based on customized smart bracelet with built-in accelerometer. PeerJ 2016, 4, e2258. Golgouneh, A.; Tarvirdizadeh, B. Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms. Neural Comput. Appl. 2020, 32, 7515–7537. Picard, R.W. Future affective technology for autism and emotion communication. Philos. Trans. R. Soc. B Biol. Sci. 2009, 364, 3575–3584. Raya, M.A.; Alice, I.; Giglioli, C.; Marín-Morales, J.; Higuera-Trujillo, J.L.; Olmos, E.; Minissi, M.E.; Garcia, G.T.; Sirera, M.; Abad, L. Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality. Front. Hum. Neurosci. 2020, 14, 90. Westeyn, T.; Presti, P.; Starner, T. ActionGSR: A Combination Galvanic Skin Response—Accelerometer for Physiological Measurements in Active Environments. In Proceedings of the 2006 10th IEEE International Symposium on Wearable Computers, Montreux, Switzerland, 11–14 October 2006; pp. 3–4. Fletcher, R.R.; Dobson, K.; Goodwin, M.S.; Eydgahi, H.; Wilder-smith, O.; Fernholz, D.; Kuboyama, Y.; Hedman, E.B.; Poh, M.; Member, S.; et al. iCalm: Wearable Sensor and Network Architecture for Wirelessly Communicating and Logging Autonomic Activity. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 215–223. Krupa, N.; Anantharam, K.; Sanker, M.; Datta, S.; Sagar Vijay, J.V. Recognition of emotions in autistic children using physiological signals. Health Technol. 2016, 6, 137–147. Gul Airija, A.; Bakhterib, R.; Khalil-Hania, M. Smart wearable stress monitoring device for autistic children. Jurnal Teknologi 2016, 5, 75–81. Akinloye Oluwayemisi, F.; Obe, O.; Boyinbode, O. Development of an affective-based e-healthcare system for autistic children. Sci. Afr. J. 2020, 9, e00514. Betancourt, M.A.; Dethorne, L.S.; Karahalios, K. Skin Conductance as an In Situ Marker for Emotional Arousal in Children with Neurodevelopmental Communication Impairments: Methodological Considerations and Clinical Implications. ACM Trans. Access. Comput. 2017, 9, 1–29. Hedman, E.; Miller, L.; Schoen, S.; Nielsen, D.; Goodwin, M.; Picard, R. Measuring autonomic arousal during therapy. In Proceedings of the 8th International Design and Emotion Conference, London, UK, 11–14 September 2012. Synnes, K.; Lilja, M.; Nyman, A.; Espinilla, M.; Cleland, I.; Comas, A.G.S.; Comas-Gonzalez, Z.; Hallberg, J.; Karvonen, N.; de Morais, W.O.; et al. H2Al—The Human Health and Activity Laboratory. Proceedings 2018, 2, 1241. Anusha, A.S.; Preejith, S.P.; Akl, T.J.; Joseph, J.; Sivaprakasam, M. Dry Electrode Optimization for Wrist-based Electrodermal Activity Monitoring. In Proceedings of the IEEE International Workshop on Medical Measurement and Applications (MEMEA), Rome, Italy, 11–13 June 2018; pp. 1–6. Kushki, A.; Fairley, J.; Merja, S.; King, G.; Chau, T. Comparison of blood volume pulse and skin conductance responses to mental and affective stimuli at different anatomical sites. Physiol. Meas. 2011, 32, 1529–1539. Kappeler-Setz, C.; Gravenhorst, F.; Schumm, J.; Arnrich, B.; Gerhard, T. Towards long term monitoring of electrodermal activity in daily life. Ubiquit. Comput. 2011, 17, 261–271. Borrego, A.; Latorre, J.; Alcaniz, M.; Llorens, R. Reliability of the Empatica E4 wristband to measure electrodermal activity to emotional stimuli. In Proceedings of the International Conference on Virtual Rehabilitation, Tel Aviv, Israel, 21–24 July 2019; pp. 3–4. Kutt, K.; Binek, W.; Misiak, P.; Nalepa, G.J.; Bobek, S. Towards the Development of Sensor Platform for Processing Physiological Data from Wearable Sensors; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; Volume 10842, ISBN 9783319912615. Sagl, G.; Resch, B.; Petutschnig, A.; Kyriakou, K.; Liedlgruber, M.; Wilhelm, F.H. Wearables and the quantified self: Systematic benchmarking of physiological sensors. Sensors 2019, 19, 4448. Poh, M.Z.; Swenson, N.C.; Picard, R.W. A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE Trans. Biomed. Eng. 2010, 57, 1243–1252. Kasos, K.; Kekecs, Z.; Csirmaz, L.; Zimonyi, S.; Vikor, F.; Kasos, E.; Veres, A.; Kotyuk, E.; Szekely, A. Bilateral comparison of traditional and alternate electrodermal measurement sites. Psychophysiology 2020, 57, 1–15. Phitayakorn, R.; Minehart, R.D.; Pian-Smith, M.C.M.; Hemingway, M.W.; Petrusa, E.R. Practicality of using galvanic skin response to measure intraoperative physiologic autonomic activation in operating room team members. Surgery 2015, 158, 1415–1420. Chen, S.T.; Lin, S.S.; Lan, C.W.; Hsu, H.Y. Design and development of awearable device for heat stroke detection. Sensors 2018, 18, 17. Camara, C.; Martín, H.; Peris-Lopez, P.; Aldalaien, M. Design and analysis of a true random number generator based on GSR signals for body sensor networks. Sensors 2019, 19, 2033. Airij, A.G.; Sudirman, R.; Sheikh, U.U.; Khuan, L.Y.; Zakaria, N.A. Significance of electrodermal activity response in children with autism spectrum disorder. Indones. J. Electr. Eng. Comput. Sci. 2020, 19, 1113–1120. Winton, W.M.; Putnam, L.E.; Krauss, R.M. Facial and autonomic manifestations of the dimensional structure of emotion. J. Exp. Soc. Psychol. 1984, 20, 195–216. Shimmer. Shimmer3 GSR+ Unit. Available online: http://www.shimmersensing.com/products/shimmer3-wireless-gsr-sensor (accessed on 27 January 2021). Empatica. E4 Wristband. Available online: https://www.empatica.com/research/e4/ (accessed on 27 January 2021). Posner, J.; Russell, J.A.; Peterson, B.S. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 2005, 17, 715–734. Russell, J.A. A circumplex model of affect. J. Pers. Soc. Psychol. 1980, 39, 1161–1178. Citron, F.M.M.; Gray, M.A.; Critchley, H.D.; Weekes, B.S.; Ferstl, E.C. Emotional valence and arousal affect reading in an interactive way: Neuroimaging evidence for an approach-withdrawal framework. Neuropsychologia 2014, 56, 79–89. Barrett, L.F.; Russell, J.A. The structure of current affect: Controversies and emerging consensus. Curr. Dir. Psychol. Sci. 1999, 8, 10–14. Benedek, M.; Kaernbach, C. A continuous measure of phasic electrodermal activity. J. Neurosci. Methods 2010, 190, 80–91. Wang, C.; Baird, T.; Huang, J.; Coutinho, J.D.; Brien, D.C.; Munoz, D.P.; Wang, C. Arousal Effects on Pupil Size, Heart Rate, and Skin Conductance in an Emotional Face Task. Front. Neurol. 2018, 9, 1–13. Kianimajd, A.; Ruano, M.G.; Carvalho, P.; Henriques, J.; Rocha, T.; Paredes, S.; Ruano, A.E. Comparison of different methods of measuring similarity in physiologic time series. IFAC PapersOnline 2017, 50, 11005–11010. van Dooren, M.; de Vries, J.J.G.G.J.; Janssen, J.H. Emotional sweating across the body: Comparing 16 different skin conductance measurement locations. Physiol. Behav. 2012, 106, 298–304. Payne, A.F.H.; Schell, A.M.; Dawson, M.E. Lapses in skin conductance responding across anatomical sites: Comparison of fingers, feet, forehead, and wrist. Psychophysiology 2016, 53, 1084–1092. Boucsein, W. Electrodermal Activity, 2nd ed.; Springer: Wuppertal, Germany, 2012; ISBN 9781461411253. Picard, R.W.; Fedor, S.; Ayzenberg, Y. Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry. Emot. Rev. 2016, 8, 62–75. Kasos, K.; Zimonyi, S.; Kasos, E.; Lifshitz, A.; Varga, K.; Szekely, A. Does the Electrodermal System “Take Sides” When It Comes to Emotions? Appl. Psychophysiol. Biofeedback 2018, 43, 203–210. Bjørhei, A.; Pedersen, F.T.; Muhammad, S.; Tronstad, C.; Kalvøy, H.; Wojniusz, S.; Pabst, O.; Sütterlin, S. An investigation on bilateral asymmetry in electrodermal activity. Front. Behav. Neurosci. 2019, 13, 1–11. Banganho, A.R.; Dos Santos, M.B.; Da Silva, H.P. Design and Evaluation of an Electrodermal Activity Sensor (EDA) with Adaptive Gain. IEEE Sens. J. 2021, 21, 8639–8649. Toyokura, M. Waveform variation and size of sympathetic skin response: Regional difference between the sole and palm recordings. Clin. Neurophysiol. 1999, 110, 765–771.
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spelling	Sanchez-Comas, AndresSynnes, KåreMolina Estren, DiegoTroncoso Palacio, AlexanderComas Gonzalez, Zhoe2021-06-24T18:09:09Z2021-06-24T18:09:09Z2021-06-19https://hdl.handle.net/11323/8410https://doi.org/10.3390/s21124210Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.Sanchez-Comas, Andres-will be generated-orcid-0000-0002-4280-8070-600Synnes, Kåre-will be generated-orcid-0000-0003-4549-6751-600Molina Estren, Diego-will be generated-orcid-0000-0003-4084-7567-600Troncoso Palacio, Alexander Humberto-will be generated-orcid-0000-0001-6034-695X-600Comas Gonzalez, Zhoe-will be generated-orcid-0000-0001-7151-5245-600application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sensorshttps://www.mdpi.com/1424-8220/21/12/4210stresswearablesensorphysiological signalsgalvanic skin responseGSRelectrodermal activityEDApleasant and unpleasant stimulivalencecorrelationCorrelation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuliArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionKumari, P.; Mathew, L.; Syal, P. Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens. Bioelectron. 2017, 90, 298–307.Ni, Q.; Hernando, A.B.G.; de la Cruz, I.P. The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development. Sensors 2015, 15, 11312–11362. [Peetoom, K.K.B.; Lexis, M.A.S.; Joore, M.; Dirksen, C.D.; de Witte, L.P. Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil. Rehabil. Assist. Technol. 2015, 10, 271–294.Jekel, K.; Damian, M.; Storf, H.; Hausner, L.; Frolich, L. Development of a Proxy-Free Objective Assessment Tool of Instrumental Activities of Daily Living in Mild Cognitive Impairment Using Smart Home Technologies. J. Alzheimer Dis. 2016, 52, 509–517.Coronato, A.; de Pietro, G.; Paragliola, G. A situation-aware system for the detection of motion disorders of patients with autism spectrum disorders. Expert Syst. Appl. 2014, 41, 7868–7877.Vanus, J.; Belesova, J.; Martinek, R.; Nedoma, J.; Fajkus, M.; Bilik, P.; Zidek, J. Monitoring of the daily living activities in smart home care. Hum. Cent. Comput. Inf. Sci. 2017, 7, 1–34.Sanchez-Comas, A.; Synnes, K.; Hallberg, J. Hardware for recognition of human activities: A review of smart home and AAL related technologies. Sensors 2020, 20, 4427.Fernández-Caballero, A.; Martínez-Rodrigo, A.; Pastor, J.M.; Castillo, J.C.; Lozano-Monasor, E.; López, M.T.; Zangróniz, R.; Latorre, J.M.; Fernández-Sotos, A. Smart environment architecture for emotion detection and regulation. J. Biomed. Inform. 2016, 64, 55–73.Mendoza-Palechor, F.; Menezes, M.L.; Sant’Anna, A.; Ortiz-Barrios, M.; Samara, A.; Galway, L. Affective recognition from EEG signals: An integrated data-mining approach. J. Ambient Intell. Humaniz. Comput. 2019, 10, 3955–3974.Menezes, M.L.R.; Samara, A.; Galway, L.; Sant’Anna, A.; Alonso-Fernandez, F.; Wang, H.; Bond, R. Towards emotion recognition for virtual environments: An evaluation of eeg features on benchmark dataset. Pers. Ubiquitous Comput. 2017, 21, 1003–1013.Raheel, A.; Majid, M.; Alnowami, M.; Anwar, S.M. Physiological sensors based emotion recognition while experiencing tactile enhanced multimedia. Sensors 2020, 20, 4037.Kang, J.; Larkin, H. Application of an Emergency Alarm System for Physiological Sensors Utilizing Smart Devices. Technologies 2017, 5, 26.Zhang, Z.; Song, Y.; Cui, L.; Liu, X. Emotion recognition based on customized smart bracelet with built-in accelerometer. PeerJ 2016, 4, e2258.Golgouneh, A.; Tarvirdizadeh, B. Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms. Neural Comput. Appl. 2020, 32, 7515–7537.Picard, R.W. Future affective technology for autism and emotion communication. Philos. Trans. R. Soc. B Biol. Sci. 2009, 364, 3575–3584.Raya, M.A.; Alice, I.; Giglioli, C.; Marín-Morales, J.; Higuera-Trujillo, J.L.; Olmos, E.; Minissi, M.E.; Garcia, G.T.; Sirera, M.; Abad, L. Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality. Front. Hum. Neurosci. 2020, 14, 90.Westeyn, T.; Presti, P.; Starner, T. ActionGSR: A Combination Galvanic Skin Response—Accelerometer for Physiological Measurements in Active Environments. In Proceedings of the 2006 10th IEEE International Symposium on Wearable Computers, Montreux, Switzerland, 11–14 October 2006; pp. 3–4.Fletcher, R.R.; Dobson, K.; Goodwin, M.S.; Eydgahi, H.; Wilder-smith, O.; Fernholz, D.; Kuboyama, Y.; Hedman, E.B.; Poh, M.; Member, S.; et al. iCalm: Wearable Sensor and Network Architecture for Wirelessly Communicating and Logging Autonomic Activity. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 215–223.Krupa, N.; Anantharam, K.; Sanker, M.; Datta, S.; Sagar Vijay, J.V. Recognition of emotions in autistic children using physiological signals. Health Technol. 2016, 6, 137–147.Gul Airija, A.; Bakhterib, R.; Khalil-Hania, M. Smart wearable stress monitoring device for autistic children. Jurnal Teknologi 2016, 5, 75–81.Akinloye Oluwayemisi, F.; Obe, O.; Boyinbode, O. Development of an affective-based e-healthcare system for autistic children. Sci. Afr. J. 2020, 9, e00514.Betancourt, M.A.; Dethorne, L.S.; Karahalios, K. Skin Conductance as an In Situ Marker for Emotional Arousal in Children with Neurodevelopmental Communication Impairments: Methodological Considerations and Clinical Implications. ACM Trans. Access. Comput. 2017, 9, 1–29.Hedman, E.; Miller, L.; Schoen, S.; Nielsen, D.; Goodwin, M.; Picard, R. Measuring autonomic arousal during therapy. In Proceedings of the 8th International Design and Emotion Conference, London, UK, 11–14 September 2012.Synnes, K.; Lilja, M.; Nyman, A.; Espinilla, M.; Cleland, I.; Comas, A.G.S.; Comas-Gonzalez, Z.; Hallberg, J.; Karvonen, N.; de Morais, W.O.; et al. H2Al—The Human Health and Activity Laboratory. Proceedings 2018, 2, 1241.Anusha, A.S.; Preejith, S.P.; Akl, T.J.; Joseph, J.; Sivaprakasam, M. Dry Electrode Optimization for Wrist-based Electrodermal Activity Monitoring. In Proceedings of the IEEE International Workshop on Medical Measurement and Applications (MEMEA), Rome, Italy, 11–13 June 2018; pp. 1–6.Kushki, A.; Fairley, J.; Merja, S.; King, G.; Chau, T. Comparison of blood volume pulse and skin conductance responses to mental and affective stimuli at different anatomical sites. Physiol. Meas. 2011, 32, 1529–1539.Kappeler-Setz, C.; Gravenhorst, F.; Schumm, J.; Arnrich, B.; Gerhard, T. Towards long term monitoring of electrodermal activity in daily life. Ubiquit. 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Correlation analysis of different measurement places of galvanic skin response in test groups facing pleasant and unpleasant stimuli

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