The DOI matches its Crossref record; 74 of the 75 references it cites were found in a registry or an index.
Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review
Open accessAbstract
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Two components that contribute to measurable changes in EDA have been described. The fast galvanic skin responses, also referred to as phasic responses, act in the order of 0.5 to 5 s, whereas the tonic component, which expresses in level changes, acts in the order of minutes26,27”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [26]
In the citing paper
“Motion artifacts are a primary cause of bad signal quality, and research has suggested various methods for mitigating these26,39,40.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Boucsein, W. (2012) · doi:10.1007/978-1-4614-1126-0 · Cited by 2,526
Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
Healey, J. A., & Picard, R. W. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2005.848368 · Cited by 2,207
Dawson, M. E., Schell, A. M., Filion, D. L., & Berntson, G. G. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546396.007 · Cited by 1,776
Publication Recommendations for Electrodermal Measurements
Fowles, D. C., Christie, M. J., Edelberg, R., Grings, W. W., Lykken, D. T., & Venables, P. H. (1981) · Psychophysiology · doi:10.1111/j.1469-8986.1981.tb03024.x · Cited by 1,700
A continuous measure of phasic electrodermal activity
Benedek, M., & Kaernbach, C. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.04.028 · Cited by 1,689
Publication recommendations for electrodermal measurements
Society for Psychophysiological Research Ad Hoc Committee on Electrodermal Measures (2012) · Psychophysiology · doi:10.1111/j.1469-8986.2012.01384.x · Cited by 1,288
Discriminating Stress From Cognitive Load Using a Wearable EDA Device
Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., & Ehlert, U. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2036164 · Cited by 742
A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity
Poh, M.-Z., Swenson, N. C., & Picard, R. W. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2038487 · Cited by 693
cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing
Greco, A., Valenza, G., Lanatà, A., Scilingo, E. P., & Citi, L. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2474131 · Cited by 646
Decomposition of skin conductance data by means of nonnegative deconvolution
Benedek, M., & Kaernbach, C. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2009.00972.x · Cited by 594
Flagged first, then by citations
1 of the 75 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Cacioppo, J.T., Tassinary, L.G., and Berntson, G. (2007). Handbook of Psychophysiology, Cambridge University Press. [3rd ed.].
- Martin, I., and Venables, P.H. (1980). Techniques in Psychophysiology, John Wiley & Sons. Psychological Medicine.
- Hugdahl, K. (1995). Psychophysiology: The Mind–Body Perspective, Harvard University Press.
- Grings, 1974, Recording of electrodermal phenomena, Methods Physiol. Psychol., 1, 273
- Boucsein, W. (2012). Electrodermal Activity, Springer Science & Business Media. · DOI · Paper page
Cited by
Sorted by citations
Items 201–300 of 526
Multi-level Pain Quantification using a Smartphone and Electrodermal Activity
Kong, Y., Posada-Quintero, H. F., & Chon, K. H. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871228 · Cited by 9
Self-citationBalch, A., Cardei, M. A., Kranz, S., & Doryab, A. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3743690 · Cited by 10
Learning performance and physiological feedback-based evaluation for human–robot collaboration
Lin, C. J., & Lukodono, R. P. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104425 · Cited by 9
Rivers, D. J. (2022) · Frontiers in Education · doi:10.3389/feduc.2022.802639 · Cited by 8
Simulation of ambulatory electrodermal activity and the handling of low-quality segments
Pattyn, E., Thammasan, N., Lutin, E., Tourolle, D., van Kraaij, A., Kosunen, I., Raedt, W. D., & Hoof, C. A. V. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107859 · Cited by 8
Electrodermal Activity: Applications and Challenges
Hossain, M.-B., Kong, Y., Posada-Quintero, H. F., & Chon, K. H. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009000796.022 · Cited by 8
Self-citationDifferentiating presence in virtual reality using physiological signals
Saha, S., Dobbins, C., Gupta, A., & Dey, A. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102065 · Cited by 9
Smartwatches for non‐invasive hypoglycaemia detection during cognitive and psychomotor stress
Maritsch, M., Föll, S., Lehmann, V. F., Styger, N., Bérubé, C., Kraus, M., Feuerriegel, S., Kowatsch, T., Züger, T., Fleisch, E., et al. (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15402 · Cited by 8
Combined Effects of the Visual–Acoustic Environment on Public Response in Urban Forests
Lan, Y., Tang, Y., Liu, Z. S., Yao, X., Zhu, Z., Liu, F., Li, J., Dong, J., & Chen, Y. (2024) · Forests · doi:10.3390/f15050858 · Cited by 8
Badura, A., Bienkowska, M., Myśliwiec, A. P., & Piętka, E. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3461589 · Cited by 8
A Portable Device Performing Continuous Impedance Spectroscopy for Skin Conductivity
Massot, B., Desmazure, E., Montalibet, A., McAdams, E. T., & Géhin, C. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3485187 · Cited by 8
Mercado-Diaz, L., Veeranki, Y. R., Large, E. W., & Posada-Quintero, H. F. (2024) · Italian National Conference on Sensors · doi:10.3390/s24248130 · Cited by 6
Self-citationKong, Y., Posada-Quintero, H. F., & Chon, K. H. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630637 · Cited by 8
Self-citationPosada-Quintero, H. F., Derrick, B. J., Ellis, M. C., Natoli, M. J., Winstead‐Derlega, C., Gonzalez, S. I., Allen, C. M., Makowski, M. S., Keuski, B. M., Moon, R. E., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00688-4 · Cited by 7
Self-citationCan contextualized physics problems enhance student motivation?
Wei, Y., Peng, X., Zhong, Y., Pi, F., Zhai, Y., & Bao, L. (2025) · Physical Review Physics Education Research · doi:10.1103/2g1b-hmhq · Cited by 7
Assessing pedestrian stress with biometric sensing and survey responses
Ma, S., Zhang, W., Noland, R. B., Andrews, C. J., Younes, H., & Hagen, L. A. V. (2025) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.103347 · Cited by 7
Xu, Z., Liu, S., Zhang, S., & Yang, Y. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104618 · Cited by 7
Višňovcová, Z., Ferencová, N., Grendár, M., Ondrejka, I., Olexova, L. B., Bujnakova, I., & Tonhajzerová, I. (2023) · General Physiology and Biophysics · doi:10.4149/gpb_2022011 · Cited by 8
Hossain, M.-B., Golzari, K., Kong, Y., Derrick, B. J., Moon, R. E., Natoli, M. J., Ellis, M. C., Winstead‐Derlega, C., Gonzalez, S. I., Allen, C. M., et al. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.03.004 · Cited by 7
Self-citationNandipati, K. K., Pal, S., & Mitra, R. (2024) · Sensors · doi:10.3390/s24144565 · Cited by 8
Lee, J., Kishino, S., & Suzuki, K. (2021) · International Journal of Automotive Engineering · doi:10.20485/jsaeijae.12.2_32 · Cited by 7
Video-based sympathetic arousal assessment via peripheral blood flow estimation
Braun, B., McDuff, D., Baltrušaitis, T., & Holz, C. (2023) · Biomedical Optics Express · doi:10.1364/boe.507949 · Cited by 7
Li, A., Xue, C., Wu, R., Wu, W. D., Zhao, J., & Qiang, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2327179 · Cited by 7
Identifying Opioid Withdrawal Using Wearable Biosensors
Kulman, E., Venkatasubramanian, K., Chapman, B. P., & Carreiro, S. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.435 · Cited by 7
Bouhdana, I., Charland, P., Foisy, L. B., Lapierre, H. G., Léger, P., Allaire‐Duquette, G., Potvin, P., Masson, S., Riopel, M., & Mahhou, M. A. (2023) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2023.100199 · Cited by 7
A multimodal drowsiness dataset using video, biometric, and behavioral data
Bodaghi, M., Hosseini, M., Gottumukkala, R. N., Bhupatiraju, R. T., Ahmad, I., & Gabbouj, M. (2026) · Scientific Data · doi:10.1038/s41597-025-06540-1 · Cited by 7
Baghestani, F., Kong, Y., Chen, I., D'Angelo, W., & Chon, K. H. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3565174 · Cited by 7
Self-citationFuzzy-Based Sensor Fusion for Cognitive Load Assessment in Inclusive Manufacturing Strategies
Testa, A., Simeone, A., Zecca, M., Paoli, A., & Settineri, L. (2025) · Sensors · doi:10.3390/s25113356 · Cited by 6
Enhanced PPG-based stress detection: a multivariate cross-dataset analysis across devices and tasks
Cui, X., Sun, Z., Chen, Z., & Peng, C. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108149 · Cited by 6
Ţîțu, A. M., & Pop, A. B. (2025) · Sustainability · doi:10.3390/su17209100 · Cited by 6
Immersive competence as a source of bias in virtual reality clinical assessment
Schaal, J., Leutritz, T., Lindner, M., Zamzow, A., Backhaus, J., Koenig, S., & Mühling, T. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02482-z · Cited by 6
Veeranki, Y. R., Rao, N. K., Posada-Quintero, H. F., & Swaminathan, R. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.03.009 · Cited by 6
Self-citationRah, A., & Chen, Y. (2026) · Sensors · doi:10.3390/s26102983 · Cited by 6
Electrodermal Activity for Emotion Recognition Using CNN and Bi-GRU Model
Zhu, L., Spachos, P., & Gregori, S. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278603 · Cited by 6
Pilot Study on the Relationship Between Acceptance of Collaborative Robots and Stress
Lutin, E., Elprama, S., Cornelis, J. P., Leconte, P., Doninck, B. V., Witters, M., Raedt, W. D., & Jacobs, A. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01156-8 · Cited by 6
Transition Network-Based Analysis of Electrodermal Activity Signals for Emotion Recognition
Veeranki, Y. R., Posada-Quintero, H. F., & Swaminathan, R. (2024) · IRBM · doi:10.1016/j.irbm.2024.100849 · Cited by 6
Self-citationRevisiting the Venoarteriolar Reflex–Further Insights from Upper Limb Dependency in Healthy Subjects
Silva, H., & Rezendes, C. (2024) · Biology · doi:10.3390/biology13090715 · Cited by 6
Vallès-Català, T., & Guerrero, I. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2474464 · Cited by 6
Queirolo, L., Facco, E., Roccon, A., Pistollato, E., Fiore, A. D., Fazia, T., Bacci, C., & Zanette, G. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1577325 · Cited by 6
Yu, Z., Sherpa, M. T., Iadarola, S., Shamlian, K. D., Daley, S. G., Levine, G., Bajorski, P., & Zheng, Z. (2023) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3565066.3608686 · Cited by 6
Ojha, A., Sharifironizi, A., Liu, Y., & Jebelli, H. (2024) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784485262.017 · Cited by 6
Does Heart Rate Variability Predict Impairment of Operational Performance in Divers?
Freiberger, J. J., Derrick, B. J., Chon, K. H., Hossain, M.-B., Posada-Quintero, H. F., Cooter, M. L., & Moon, R. E. (2024) · Sensors · doi:10.3390/s24237726 · Cited by 5
Self-citationLi, J. (2025) · International Journal of Information and Communication Technology · doi:10.1504/ijict.2025.10071345 · Cited by 5
Nguyen, D. M. A., Nguyen, T. M. T., Lim, K. Y. T., & Posada-Quintero, H. F. (2025) · AI Brain and Child · doi:10.1007/s44436-025-00007-1 · Cited by 5
Self-citationAssessment of Resilience of the Hellenic Navy Seals by Electrodermal Activity during Cognitive Tasks
Mourtakos, S., Vassiliou, G., Kontoangelos, K. A., Papageorgiou, C. C., Philippou, Α., Bersimis, F., Geladas, N. D., Koutsilieris, M., Sidossis, L. S., Tsirmpas, C., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18084384 · Cited by 5
Hamilton, R. I., Garcia, A. A., Bowd, J., Hamilton, D. F., Mason, D. J., Elliott, M. T., & Holt, C. (2024) · BMC Research Notes · doi:10.1186/s13104-024-06689-9 · Cited by 5
Ghaderyan, P., & Mirzaeian, R. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106679 · Cited by 5
Electrodermal activity and its molecular mechanisms: Unraveling insights into skin diseases
Zhu, X., Song, J., Liu, T., Huang, S., & Yao, B. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100085 · Cited by 5
COMPLEXITY-BASED ANALYSIS OF THE VARIATIONS IN SKIN REACTION IN DIFFERENT EMOTIONAL STATES
Krejcar, O., PANDEY, A., & NAMAZI, H. (2024) · Fractals · doi:10.1142/s0218348x25500094 · Cited by 5
LumEDA: image luminance based contactless correlates of electrodermal responses
Bhamborae, M. J., Schneider, E. N., Flotho, P., Francis, A. L., & Strauß, D. J. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adb369 · Cited by 5
Emotional Reactions To AI-Generated Images: A Pilot Study Using Neurophysiological Measures
Bilucaglia, M., Casiraghi, C., Bruno, A., Chiarelli, S., Fici, A., Russo, V., & Zito, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82487-6_11 · Cited by 6
A new trauma severity scoring system adapted to wearable monitoring: A pilot study
Lemarquand, A., Jannot, P., Kammerlocher, L., Lissorgues, G., Behr, M., Arnoux, P., & Boussen, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318290 · Cited by 5
Lu, L., Su, B., Jung, S., Qing, L., & Xu, X. (2025) · Ergonomics · doi:10.1080/00140139.2025.2497523 · Cited by 5
A new measurement for workload assessment in agricultural tasks: EDA-based real-time model
Seong, S., Park, J., & Kim, J. H. (2025) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2025.103771 · Cited by 5
Faust, L. J., Cui, J., Knepper, C., Nasseri, M., Worrell, G. A., & Brinkmann, B. H. (2025) · Sensors · doi:10.3390/s25175562 · Cited by 6
Kong, Y., Baghestani, F., D’Angelo, W., Chen, I.-P., & Chon, K. H. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3590517 · Cited by 5
Self-citationSingaram, S., Ramakrishnan, K., & Periyasamy, S. (2023) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/09544119231184113 · Cited by 5
Li, X., Long, Y., Zhang, S., Yang, C., Xing, M., & Zhang, S. (2024) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-024-09651-4 · Cited by 6
Angelucci, A., & Aliverti, A. (2024) · Mechanisms and machine science · doi:10.1007/978-3-031-63755-1_10 · Cited by 5
Autoencoder Based Nonlinear Feature Extraction from EDA Signals for Emotion Recognition
Veeranki, Y. R., Díaz, L. R. M., & Posada-Quintero, H. F. (2024) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea60663.2024.10596800 · Cited by 5
Self-citationElementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity
Subramanian, S., Purdon, P. L., Barbieri, R., & Brown, E. N. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009099 · Cited by 5
Anxiety during the dental care of children aged 4 to 6 years over three consecutive visits
Fux‐Noy, A., Zeineh, R., Shmueli, A., Halperson, E., Ram, D., & Moskovitz, M. (2023) · Journal of Clinical Pediatric Dentistry · doi:10.22514/jocpd.2023.084 · Cited by 5
Reid, C., Keighrey, C., Murray, N., Dunbar, R., & Buckley, J. (2020) · Sensors · doi:10.3390/s20236857 · Cited by 5
Regalia, G., Resnati, D., & Tognetti, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822548-6.00130-8 · Cited by 9
EDA-graph: Graph Signal Processing of Electrodermal Activity for Emotional States DetectionPreprint
Díaz, L. R. M., Veeranki, Y. R., Marmolejo‐Ramos, F., & Posada-Quintero, H. F. (2023) · doi:10.36227/techrxiv.24311716.v1 · Cited by 5
Self-citationLinking Cancer Pain Features and Biosignals for Automatic Pain Assessment
Cascella, M., Perri, F., Ottaiano, A., Santorsola, M., Marciano, M. L., Rampetta, F. R., Pontone, M., Crispo, A., Sabbatino, F., Franci, G., et al. (2026) · Cancers · doi:10.3390/cancers18040646 · Cited by 4
Zikos, L., Degraeve, B., Pinti, A., Poupart, J., Norberciak, L., Kwiatkowski, A., Donzé, C., & Lenne, B. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1404876 · Cited by 4
Kong, Y., McNaboe, R. Q., Hossain, M.-B., Posada-Quintero, H. F., Diaz, K., Chon, K. H., & Bolkhovsky, J. (2025) · Biosensors · doi:10.3390/bios15020078 · Cited by 4
Self-citationForood, A. M. K., Pandian, S. S. R., McNaboe, R. Q., Lachos, T. D. C., Lantigua, D. O., & Posada-Quintero, H. F. (2025) · Micromachines · doi:10.3390/mi16111255 · Cited by 6
Self-citationElectrodermal Activity: The Potential of Psychophysiology in Pain Assessment
Papanikolaou, F., Nedergaard, R. B., Drewes, A. M., & Benthien, K. S. (2026) · Psychophysiology · doi:10.1111/psyp.70276 · Cited by 4
Responsible Trauma Research: Designing Effective and Sustainable Virtual Reality Exposure Studies
Degenhard, A., Ppali, S., Liarokapis, F., Rukzio, E., Spohrs, J., & Tschoeke, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791964 · Cited by 4
Dharmansyah, D., Rahayuwati, L., Pramukti, I., & Mutyara, K. (2026) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s606165 · Cited by 4
Xu, R., Cao, S. C., Barnett‐Cowan, M., Irving, E. L., Niechwiej‐Szwedo, E., & Kearns, S. K. (2026) · Sensors · doi:10.3390/s26123627 · Cited by 4
Klein, L. A., Doyle, F. L., Northam, J. C., Eapen, V., Frick, P. J., Kimonis, E. R., Hawes, D. J., Moul, C., Richmond, J. L., Mehta, D., et al. (2021) · Development and Psychopathology · doi:10.1017/s0954579421001553 · Cited by 5
Fitness, performance, and cardiac autonomic responses to exercise in people with epilepsy
van den Bongard, F., Coenen, J., & Reinsberger, C. (2022) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2022.108869 · Cited by 4
Towards Continuous Acute Pain Detection using Deep Learning and Electrodermal Activity
Pinzon-Arenas, J. O., & Posada-Quintero, H. F. (2022) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi56158.2022.9926741 · Cited by 4
Self-citationStochastic ACF-ZCL model of time-series signals with an application to restfulness assessment
Nandy, S., Sarkar, A., Goswami, B., & Ghosh, R. (2023) · Measurement · doi:10.1016/j.measurement.2023.112641 · Cited by 4
Enhancing Emotion Recognition: Machine Learning with Phasic Spectrogram Texture Features
Sriram Kumar, P., & Ronickom, J. F. A. (2023) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla58983.2023.10346619 · Cited by 4
Šorli, J., & Lenasi, H. (2023) · Life · doi:10.3390/life14010056 · Cited by 4
Krasnodębska, P., Szkiełkowska, A., Pollak, A. M., Romaniszyn‐Kania, P., Bugdol, M. N., Bugdol, M. D., & Mitas, A. W. (2024) · International Journal of Occupational Medicine and Environmental Health · doi:10.13075/ijomeh.1896.02272 · Cited by 4
Koch, A. K., Schröter, M., Berschick, J., Schiele, J. K., Bogdanski, M., Steinmetz, M., Stritter, W., Voss, A., Seifert, G., & Keßler, C. S. (2024) · Trials · doi:10.1186/s13063-024-08491-1 · Cited by 4
Liu, H., Bin, G., Wang, X., Luo, J., & Liu, M. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1462077 · Cited by 4
Evaluating Startle in Aviation: A Focus on Instrumentation and Measurement Techniques
Romano, F., Tritto, M., Cesare, M. G. D., Nocco, S., & Cardone, D. (2024) · IEEE International Workshop on Technologies for Defense and Security (TechDefense) · doi:10.1109/techdefense63521.2024.10863269 · Cited by 4
Siche-Pantel, F., Mühlenberg, M., Buschfort, R., Michels, H., Jakobsmeyer, R., Oesterschlink, J., & Reinsberger, C. (2025) · BMC Neurology · doi:10.1186/s12883-025-04281-7 · Cited by 4
Rodrigues, T. B., Catháin, C. Ó., O'Connor, N. E., & Murray, N. (2022) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct57072.2022.00067 · Cited by 4
Sriram Kumar, P., Govarthan, P. K., Ganapathy, N., & Ronickom, J. F. A. (2023) · Studies in health technology and informatics · doi:10.3233/shti230067 · Cited by 4
Driver Drowsy and Yawn System Alert Using Deep Cascade Convolution Neural Network DCCNN
Abbood, Z. A., & Yonan, J. F. (2023) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2023.04.04.010 · Cited by 4
A Study of the Behavior and Responsibility of Slovak Drivers, Especially in Case of Fatigue
Hajdučík, A., Medvecký, Š., Hrček, S., & Klarák, J. (2021) · Applied Sciences · doi:10.3390/app11178249 · Cited by 4
Hossain, M.-B., Posada-Quintero, H. F., & Chon, K. H. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871635 · Cited by 4
Self-citationNeuroendocrine and psychophysiological investigation of the evolutionary roots of gossip
Rudnicki, K., Spacova, I., Backer, C. J. S. D., Dricot, C. E. M. K., Lebeer, S., & Poels, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-30126-9 · Cited by 5
Marchis, C. D., Crupi, G., Donato, N., & Baldari, S. (2023) · Electronics · doi:10.3390/electronics12204284 · Cited by 4
Cosic, K., Kuljanac, A., & Palermo, S. (2026) · Frontiers in psychology · doi:10.3389/fpsyg.2026.1821242 · Cited by 3
Trapero‐Asenjo, S., Fernández‐Guinea, S., Guillot, A., Pecos‐Martín, D., & Núñez-Nagy, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316723 · Cited by 3
Shared autonomy for cognitive load regulation in autonomous wheelchairs
Guo, J., Hu, Y., Huang, Q., Wang, A., & Zhang, X. (2025) · Cognition Technology & Work · doi:10.1007/s10111-025-00825-6 · Cited by 3
Sahoh, B., Wongsontham, F., Tipsavak, A., Chaithong, P., Kliangkhlao, M., Efendi, M. A., Songnuy, T., & Punsawad, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108972 · Cited by 3
Shahid, A., Alshibani, A., Al-Quraishi, M. S., Umer, W., & Mohammed, A. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103983 · Cited by 3
Short-Term Detection of Dynamic Stress Levels in Exergaming with Wearables
Masi, G., Amprimo, G., Rechichi, I., Olmo, G., & Ferraris, C. (2025) · Sensors · doi:10.3390/s25216572 · Cited by 3
Bidirectional Human–Drone Safety Communication in Construction
Zhu, Z., Jeelani, I., & Gheisari, M. (2025) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-17106 · Cited by 3
Real-time generative AI experience of the Vanitas painting
Ortiz, A., Cebral-Loureda, M., Hernandez-Morales, J., Torres-Huitzil, C., & Ramírez-Moreno, M. A. (2025) · Cogent Arts and Humanities · doi:10.1080/23311983.2025.2591387 · Cited by 3
The role of personality in multi-domain environmental perception: An occupant-centric approach
Chiucchiù, A., Gnecco, V. M., Pigliautile, I., & Pisello, A. L. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.114140 · Cited by 3
Metadata
- Authors
- Posada-Quintero, H. F., & Chon, K. H.
- Affiliations
- University of Connecticut (US)
- Corresponding author
- Chon, Ki H.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-01-15
- Volume
- 20
- Issue
- 2
- Pages
- 479
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7014446
- OpenAlex
- W3000402017
- Cited by
- 525
- References
- 75
- Citations by year
- 2026: 103 · 2025: 92 · 2024: 109 · 2023: 85 · 2022: 57 · 2021: 57 · 2020: 22
- FWCI
- 9.90
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Office of Naval Research (N00014-19-1-2209)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- electrodermal activity, sympathetic nervous system, sweat glands, psychophysiology, fatigue analysis, pain assessment, sleepiness, post-exercise recovery, epilepsy diagnosis, neuropathy, depression
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review