{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T07:55:29Z","timestamp":1782892529847,"version":"3.54.5"},"reference-count":58,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"German Federal Ministry of Education and Research (BMBF)","doi-asserted-by":"publisher","award":["01DS19008A"],"award-info":[{"award-number":["01DS19008A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.<\/jats:p>","DOI":"10.3390\/s23041959","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:09:59Z","timestamp":1675994999000},"page":"1959","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8610-2291","authenticated-orcid":false,"given":"Philip","family":"Gouverneur","sequence":"first","affiliation":[{"name":"Institute of Medical Informatics, University of L\u00fcbeck, Ratzeburger Allee 160, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2110-4207","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of L\u00fcbeck, Ratzeburger Allee 160, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1843-5152","authenticated-orcid":false,"given":"Kimiaki","family":"Shirahama","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, Kindai University, Higashiosaka 577-8502, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2504-4766","authenticated-orcid":false,"given":"Luisa","family":"Luebke","sequence":"additional","affiliation":[{"name":"Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of L\u00fcbeck, Ratzeburger Allee 160, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8941-8355","authenticated-orcid":false,"given":"Wac\u0142aw M.","family":"Adamczyk","sequence":"additional","affiliation":[{"name":"Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of L\u00fcbeck, Ratzeburger Allee 160, 23562 L\u00fcbeck, Germany"},{"name":"Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-065 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6351-2957","authenticated-orcid":false,"given":"Tibor M.","family":"Szikszay","sequence":"additional","affiliation":[{"name":"Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of L\u00fcbeck, Ratzeburger Allee 160, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7308-5469","authenticated-orcid":false,"given":"Kerstin","family":"Luedtke","sequence":"additional","affiliation":[{"name":"Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Institute of Health Sciences, University of L\u00fcbeck, Ratzeburger Allee 160, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4877-8287","authenticated-orcid":false,"given":"Marcin","family":"Grzegorzek","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of L\u00fcbeck, Ratzeburger Allee 160, 23562 L\u00fcbeck, Germany"},{"name":"Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-287 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hosny, K.M., Kassem, M.A., and Foaud, M.M. (2018, January 20\u201322). Skin cancer classification using deep learning and transfer learning. Proceedings of the 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, Egypt.","DOI":"10.1109\/CIBEC.2018.8641762"},{"key":"ref_2","unstructured":"Andresen, J., Kepp, T., Wang-Evers, M., Ehrhardt, J., Manstein, D., and Handels, H. (2022). Bildverarbeitung f\u00fcr die Medizin 2022, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s10096-020-03901-z","article-title":"Classification of COVID-19 patients from chest CT images using multi-objective differential evolution\u2013based convolutional neural networks","volume":"39","author":"Singh","year":"2020","journal-title":"Eur. J. Clin. Microbiol. Infect."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bielby, J., Kuhn, S., Colreavy-Donnelly, S., Caraffini, F., O\u2019Connor, S., and Anastassi, Z.A. (2020, January 19\u201324). Identifying Parkinson\u2019s disease through the classification of audio recording data. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185915"},{"key":"ref_5","unstructured":"Gouverneur, P., Jaworek-Korjakowska, J., K\u00f6ping, L., Shirahama, K., Kleczek, P., and Grzegorzek, M. Classification of physiological data for emotion recognition. Proceedings of the International Conference on Artificial Intelligence and Soft Computing."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101981","DOI":"10.1016\/j.artmed.2020.101981","article-title":"Sleep stage classification for child patients using DeConvolutional Neural Network","volume":"110","author":"Huang","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, F., Shirahama, K., Nisar, M.A., K\u00f6ping, L., and Grzegorzek, M. (2018). Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors, 18.","DOI":"10.3390\/s18020679"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Irshad, M.T., Nisar, M.A., Huang, X., Hartz, J., Flak, O., Li, F., Gouverneur, P., Piet, A., Oltmanns, K.M., and Grzegorzek, M. (2022). SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors. Sensors, 22.","DOI":"10.3390\/s22207711"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S1090-3801(98)90048-9","article-title":"Where does it hurt? Describing the body locations of chronic pain","volume":"2","author":"Davies","year":"1998","journal-title":"Eur. J. Pain"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1097\/00043426-199807000-00020","article-title":"Measurement of pain in infants and children","volume":"20","author":"Finley","year":"1998","journal-title":"J. Pediatr. Hematol.\/Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/j.pmn.2014.01.004","article-title":"Nurses assessing pain with the Nociception Coma Scale: Interrater reliability and validity","volume":"15","author":"Vink","year":"2014","journal-title":"Pain Manag. Nurs."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gruss, S., Treister, R., Werner, P., Traue, H.C., Crawcour, S., Andrade, A., and Walter, S. (2015). Pain intensity recognition rates via biopotential feature patterns with support vector machines. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140330"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Thiam, P., Bellmann, P., Kestler, H.A., and Schwenker, F. (2019). Exploring deep physiological models for nociceptive pain recognition. Sensors, 19.","DOI":"10.1101\/622431"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Prkachin, K.M., Solomon, P.E., and Matthews, I. (2011, January 21\u201325). Painful data: The UNBC-McMaster shoulder pain expression archive database. Proceedings of the 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG), Santa Barbara, CA, USA.","DOI":"10.1109\/FG.2011.5771462"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Walter, S., Gruss, S., Ehleiter, H., Tan, J., Traue, H.C., Werner, P., Al-Hamadi, A., Crawcour, S., Andrade, A.O., and da Silva, G.M. (2013, January 13\u201315). The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system. Proceedings of the 2013 IEEE International Conference on Cybernetics (CYBCO), Lausanne, Switzerland.","DOI":"10.1109\/CYBConf.2013.6617456"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Werner, P., Al-Hamadi, A., Niese, R., Walter, S., Gruss, S., and Traue, H.C. (2014, January 24\u201328). Automatic pain recognition from video and biomedical signals. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.784"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s12530-016-9158-4","article-title":"Adaptive confidence learning for the personalization of pain intensity estimation systems","volume":"8","author":"Amirian","year":"2017","journal-title":"Evol. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lopez-Martinez, D., and Picard, R. (2018, January 18\u201321). Continuous pain intensity estimation from autonomic signals with recurrent neural networks. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513575"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thiam, P., Kestler, H.A., and Schwenker, F. (2020, January 22\u201324). Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals. Proceedings of the ICPRAM, Valletta, Malta.","DOI":"10.5220\/0008896102890296"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gouverneur, P., Li, F., Adamczyk, W.M., Szikszay, T.M., Luedtke, K., and Grzegorzek, M. (2021). Comparison of feature extraction methods for physiological signals for heat-based pain recognition. Sensors, 21.","DOI":"10.3390\/s21144838"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TAFFC.2019.2892090","article-title":"Multi-modal pain intensity recognition based on the senseemotion database","volume":"12","author":"Thiam","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, X., Susam, B.T., Nezamfar, H., Diaz, D., Craig, K.D., Goodwin, M.S., Akcakaya, M., Huang, J.S., and Sa, V.R.d. (2018, January 13\u201314). Towards automated pain detection in children using facial and electrodermal activity. Proceedings of the International Workshop on Artificial Intelligence in Health, Stockholm, Sweden.","DOI":"10.1007\/978-3-030-12738-1_13"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhi, R., and Yu, J. (2019, January 24\u201326). Multi-modal fusion based automatic pain assessment. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC.2019.8785727"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Salah, A., Khalil, M.I., and Abbas, H. (2018, January 18\u201319). Multimodal pain level recognition using majority voting technique. Proceedings of the 2018 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt.","DOI":"10.1109\/ICCES.2018.8639215"},{"key":"ref_25","unstructured":"K\u00e4chele, M., Werner, P., Al-Hamadi, A., Palm, G., Walter, S., and Schwenker, F. Bio-visual fusion for person-independent recognition of pain intensity. Proceedings of the International Workshop on Multiple Classifier Systems."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Werner, P., Al-Hamadi, A., Gruss, S., and Walter, S. (2019, January 3\u20136). Twofold-multimodal pain recognition with the X-ITE pain database. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), Cambridge, UK.","DOI":"10.1109\/ACIIW.2019.8925061"},{"key":"ref_27","first-page":"1","article-title":"A machine learning approach for the identification of a biomarker of human pain using fNIRS","volume":"9","author":"Huang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1109\/JSTSP.2016.2535962","article-title":"Methods for person-centered continuous pain intensity assessment from bio-physiological channels","volume":"10","author":"Thiam","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","unstructured":"Gouverneur, P.J., Li, F., M Szikszay, T., M Adamczyk, W., Luedtke, K., and Grzegorzek, M. (2021). Information Technology in Biomedicine, Springer."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gruenewald, A., Kroenert, D., Poehler, J., Brueck, R., Li, F., Littau, J., Schnieber, K., Piet, A., Grzegorzek, M., and Kampling, H. (2018, January 29\u201331). [Regular Paper] Biomedical Data Acquisition and Processing to Recognize Emotions for Affective Learning. Proceedings of the 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE.2018.00031"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1002\/ejp.1971","article-title":"Temporal properties of pain contrast enhancement using repetitive stimulation","volume":"26","author":"Szikszay","year":"2022","journal-title":"Eur. J. Pain"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1469-8986.1991.tb03392.x","article-title":"Psychological correlates of nonspecific skin conductance responses","volume":"28","author":"Nikula","year":"1991","journal-title":"Psychophysiology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1016\/j.jpain.2022.07.007","article-title":"To calibrate or not to calibrate? A methodological dilemma in experimental pain research","volume":"23","author":"Adamczyk","year":"2022","journal-title":"J. Pain"},{"key":"ref_34","first-page":"397","article-title":"Neurophysiological examinations in neuropathic pain","volume":"27","author":"Yarnitski","year":"2006","journal-title":"Quant. Sens. Test. Handb. Clin. Neurol."},{"key":"ref_35","unstructured":"Innocente, B.P., Weingast, L.T., George, R., and Norrholm, S.D. (2020). Emotion in Posttraumatic Stress Disorder, Academic Press."},{"key":"ref_36","first-page":"797","article-title":"cvxEDA: A convex optimization approach to electrodermal activity processing","volume":"63","author":"Greco","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kong, Y., Posada-Quintero, H.F., and Chon, K.H. (2021). Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor. Sensors, 21.","DOI":"10.3390\/s21123956"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3122","DOI":"10.1109\/TBME.2021.3065218","article-title":"Sensitive physiological indices of pain based on differential characteristics of electrodermal activity","volume":"68","author":"Kong","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_41","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_43","unstructured":"Wang, Z., and Oates, T. (2015, January 25\u201331). Imaging time-series to improve classification and imputation. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"199393","DOI":"10.1109\/ACCESS.2020.3032699","article-title":"Human activity recognition based on Gramian angular field and deep convolutional neural network","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_45","unstructured":"Asesh, A. (2022). Digital Interaction and Machine Intelligence, Proceedings of the MIDI\u20192021\u20139th Machine Intelligence and Digital Interaction Conference, Warsaw, Poland, 9\u201310 December 2021, Springer."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3760","DOI":"10.1109\/TNNLS.2019.2944933","article-title":"Deep adaptive input normalization for time series forecasting","volume":"31","author":"Passalis","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yang, C.L., Chen, Z.X., and Yang, C.Y. (2019). Sensor classification using convolutional neural network by encoding multivariate time series as two-dimensional colored images. Sensors, 20.","DOI":"10.3390\/s20010168"},{"key":"ref_49","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pintelas, E., Livieris, I.E., and Pintelas, P. (2020). A grey-box ensemble model exploiting black-box accuracy and white-box intrinsic interpretability. Algorithms, 13.","DOI":"10.3390\/a13010017"},{"key":"ref_53","unstructured":"Deo, T.Y., Patange, A.D., Pardeshi, S.S., Jegadeeshwaran, R., Khairnar, A.N., and Khade, H.S. (2021). A white-box SVM framework and its swarm-based optimization for supervision of toothed milling cutter through characterization of spindle vibrations. arXiv."},{"key":"ref_54","first-page":"13","article-title":"A brief introduction and review on galvanic skin response","volume":"2","author":"Sharma","year":"2016","journal-title":"Int. J. Med. Res. Prof."},{"key":"ref_55","first-page":"1017","article-title":"A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments","volume":"49","author":"Braithwaite","year":"2013","journal-title":"Psychophysiology"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Patange, A.D., Pardeshi, S.S., Jegadeeshwaran, R., Zarkar, A., and Verma, K. (2022). Augmentation of Decision Tree Model Through Hyper-Parameters Tuning for Monitoring of Cutting Tool Faults Based on Vibration Signatures. J. Vib. Eng. Technol., 1\u201319.","DOI":"10.1007\/s42417-022-00781-9"},{"key":"ref_57","unstructured":"Katrompas, A., Ntakouris, T., and Metsis, V. Recurrence and Self-attention vs the Transformer for Time-Series Classification: A Comparative Study. Proceedings of the International Conference on Artificial Intelligence in Medicine."},{"key":"ref_58","unstructured":"(2022, November 07). Scikit-Learn Docs: KMeans. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.KMeans.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1959\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:29:11Z","timestamp":1760120951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":58,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041959"],"URL":"https:\/\/doi.org\/10.3390\/s23041959","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,9]]}}}