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Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli sensors Article Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli Andres Sanchez-Comas 1,* , Kåre Synnes 2,* , Diego Molina-Estren 3, Alexander Troncoso-Palacio 1 and Zhoe Comas-González 3 1 Department of Productivity and Innovation, Universidad de la Costa, 080 002 Barranquilla, Colombia; [email protected] 2 Department of Computer Science, Electrical and Space Engineering, Luleå Tekniska Universitet, 971 87 Luleå, Sweden 3 Department of Computer Science and Electronics, Universidad de la Costa, 080 002 Barranquilla, Colombia; [email protected] (D.M.-E.); [email protected] (Z.C.-G.) * Correspondence: [email protected] (A.S.-C.); [email protected] (K.S.); Tel.: +57-3174952457 (A.S.-C.) Abstract: 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 Citation: Sanchez-Comas, A.; Synnes, K.; Molina-Estren, D.; a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the Troncoso-Palacio, A.; Comas- GSR biosignals measured from different body sites. We experimented with the right and left wrist, González, Z. Correlation Analysis left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results of Different Measurement Places of indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers Galvanic Skin Response in Test and right foot. The results also suggested that due to a significantly strong correlation among the Groups Facing Pleasant and inner side of the right foot and the left fingers, as well as the moderate correlations with the right and Unpleasant Stimuli. Sensors 2021, 21, left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. 4210. https://doi.org/10.3390/ We also discuss some possible causes of weak and negative correlations from anomalies detected in s21124210 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. Academic Editors: Nilanjan Sarkar, Zhi Zheng and Ahmed Keywords: stress; wearable; sensor; physiological signals; galvanic skin response; GSR; electrodermal Toaha Mobashsher activity; EDA; pleasant and unpleasant stimuli; valence; correlation Received: 22 April 2021 Accepted: 15 June 2021 Published: 19 June 2021 1. Introduction Publisher’s Note: MDPI stays neutral In recent decades, advances in data analytics techniques and sensor hardware develop- with regard to jurisdictional claims in ment have led to the emergence of new research fields such as activity recognition, taking published maps and institutional affil- advantage of technologies such as smartphones, wearable devices, Internet of Things, and iations. any device with sensors or embedded systems and digital storage or streaming capacity [1]. All of this has generated a wide range of new applications, from computer science to other fields such as elderly independent living [2,3], treatment of cognitive diseases [4], autism lifestyles [5], and care [6], at which this work is motivated. Human activity recognition Copyright: © 2021 by the authors. (HAR) improves these fields, supported by specialized research and development on Licensee MDPI, Basel, Switzerland. posture, gesture, localization, occupancy, and emotion recognition [7]. This article is an open access article Emotion recognition is a field of study historically related to sociology or psychology, distributed under the terms and understanding human behavior through techniques based on subject observation, inter- conditions of the Creative Commons views, and spoken or written expression traditionally created by experts on the matter. Attribution (CC BY) license (https:// Nevertheless, computer science has boosted this field’s development capability by applying creativecommons.org/licenses/by/ machine learning techniques to video or physiological data. In this way, some literature, 4.0/). Sensors 2021, 21, 4210. https://doi.org/10.3390/s21124210 https://www.mdpi.com/journal/sensors Sensors 2021, 21, x FOR PEER REVIEW 2 of 27 interviews, and spoken or written expression traditionally created by experts on the matter. Nevertheless, computer science has boosted this field’s development capability Sensors 2021, 21, 4210 by applying machine learning techniques to video or physiological data. In2 this of 27 way, some literature, such as [8], has proposed three essential sources of emotion detection for a smart environment: facial emotion, behavior detection, and valence/arousal detection. Nowadays,such as [8 ],wearable has proposed devices three may essential help valence/arousal sources of emotion detection, detection incorporating for a smart envi- measure featuresronment: such facial as emotion,electrocardiography behavior detection, (ECG), and electromyography valence/arousal detection. (EMG), Nowadays, electroenceph- wearable devices may help valence/arousal detection, incorporating measure features such alography (EEG), galvanic skin response (GSR), photoplethysmography (PPG), and skin as electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG), temperaturegalvanic skin (ST), response as per (GSR), many photoplethysmography literature reports [9 (PPG),–13]. Although and skin temperature multimodal (ST), measures as to perimprove many literatureemotion reportsrecognition [9–13 ].performance Although multimodal are highly measures recommended to improve [11] emotion, this study focusesrecognition on evaluating performance the areperformance highly recommended of GSR signals [11], this gathered study focusesfrom wearable on evaluating sensors in differentthe performance measurement of GSR places. signals Recent gathered advances from wearable in machine sensors learning in different techniq measurementues in the last decadeplaces. and Recent the advances miniaturization in machine of learning hardware techniques technologies in the last have decade inspired and the researchers miniatur- to keepization working of hardware on new technologies ways to haveimprove inspired human researchers activity to keeprecognition working onthrough new ways emotion recognitionto improve using human GSR activity sensor recognition technology. through A quick emotion over recognitionview of the using scientific GSR sensor literature technology. A quick overview of the scientific literature shows that GSR sensor technology shows that GSR sensor technology is attracting rising interest in development and re- is attracting rising interest in development and research, especially regarding activity and search,emotion especially recognition regarding (see Figure activity1). and emotion recognition (see Figure 1). FigureFigure 1. Trends 1. Trends in the in the scientific scientific literature literature of of human human activity recognition recognition (HAR), (HAR), emotion emotion recognition, recognition, and galvanicand galvanic skin skin response/electrodermalresponse/electrodermal activity activity (GSR/EDA (GSR/EDA)) sensors. sensors. LeftLeft graph: IndividualIndividual publication publication topics. topics.Right Rightgraph: graph: Publications Publications involvinginvolving the use the useof GSR/EDA of GSR/EDA in inHAR HAR or or emotion emotion recognition.recognition. *: *: a standarda standard for searchingfor searching queries queries in the scientificin the scientific literature litera- ture database,database, itithelps helps to to search search finding finding words words that that start start with with the same the letters.same letters. ManyMany wearable wearable devices devices are usedused to to measure measure the the GSR GSR with with various various body body places places to to measuremeasure it. it. Finding Finding a suitablesuitable place place to to measure measure GSR GSR biosignals biosignals is a valuable is a valuable contribution contribution for science and engineering to continue working on new technologies to improve quality of life, for science and engineering to continue working on new technologies to improve quality as this physiological signal is commonly used for stress-related detection [14,15]. The above of impactlife, as isthis critical physiological for specific sig populations,nal is commonly for example, used autism for stress spectrum-related disorder detection (ASD), [14,15]. Theto a whichbove theimpact computer is critical science for field specific has contributed populations, to this for stress example, detection autism through spectrum GSR dis- ordermeasurement (ASD), to forwhich people the with computer ASD working science on field computing has contributed techniques to [5 this,16]. stress Moreover, detection throughother researchers GSR measurement are working for on developingpeople with ad ASD hoc hardware working wearable on computing devices fortechniques the [5,16]ASD. Moreover, population. other Some researchers works for this are population working haveon developing proposed different ad hoc measurement hardware weara- bleplaces, devices such for as the the ASD waist population. [17], the left Some and right works wrists for [this18,19 population], and the middle, have proposed
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