{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T12:19:52Z","timestamp":1762604392919,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang basic public welfare research project","award":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"],"award-info":[{"award-number":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"]}]},{"name":"Leading Goose R&amp;D Program of Zhejiang Province","award":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"],"award-info":[{"award-number":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"]}]},{"name":"Key Research and Development Program of Zhejiang Province","award":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"],"award-info":[{"award-number":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"]}]},{"name":"General Research Projects of Zhejiang Provincial Department of Education","award":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"],"award-info":[{"award-number":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"]}]},{"name":"Sichuan Science and Technology Program","award":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"],"award-info":[{"award-number":["LGF22F030003","2022C01136","2021C01039","Y202248876","2023YFSY0041"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future.<\/jats:p>","DOI":"10.3390\/s23156815","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T10:08:14Z","timestamp":1690798094000},"page":"6815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Children\u2019s Pain Identification Based on Skin Potential Signal"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9135-8360","authenticated-orcid":false,"given":"Yubo","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"International Joint Innovation Center, Zhejiang University, Haining 314400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiadong","family":"He","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cangcang","family":"Fu","sequence":"additional","affiliation":[{"name":"Children\u2019s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Wei","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Zhejiang University, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaozhi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"International Joint Innovation Center, Zhejiang University, Haining 314400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0310-2443","authenticated-orcid":false,"given":"Jikui","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"International Joint Innovation Center, Zhejiang University, Haining 314400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weize","family":"Xu","sequence":"additional","affiliation":[{"name":"Children\u2019s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China"},{"name":"National Clinical Research Center for Child Health, Hangzhou 310052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihua","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"Children\u2019s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TAFFC.2019.2946774","article-title":"Automatic Recognition Methods Supporting Pain Assessment: A Survey","volume":"13","author":"Werner","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1093\/monist\/onx021","article-title":"Defending the IASP Definition of Pain","volume":"100","author":"Aydede","year":"2017","journal-title":"Monist"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.2174\/1874091X00903010018","article-title":"Neurobiology of Pain in Children: An Overview","volume":"3","author":"Loizzo","year":"2009","journal-title":"Open Biochem. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.pmn.2007.11.006","article-title":"Assessment, Physiological Monitoring, and Consequences of Inadequately Treated Acute Pain","volume":"9","author":"Dunwoody","year":"2008","journal-title":"Pain. Manag. Nurs."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.pmn.2019.07.005","article-title":"Pain Assessment in the Patient Unable to Self-Report: Clinical Practice Recommendations in Support of the ASPMN 2019 Position Statement","volume":"20","author":"Herr","year":"2019","journal-title":"Pain Manag. Nurs."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1111\/j.1553-2712.2009.00620.x","article-title":"Validation of the Wong-Baker FACES Pain Rating Scale in Pediatric Emergency Department Patients","volume":"17","author":"Garra","year":"2010","journal-title":"Acad. Emerg. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/RBME.2017.2777907","article-title":"A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases","volume":"11","author":"Zamzmi","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1590\/S0103-05822014000400017","article-title":"Pain assessment scales in newborns: Integrative review","volume":"32","author":"Cardoso","year":"2014","journal-title":"Rev. Paul. De Pediatr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.clp.2019.08.005","article-title":"Assessment of Pain in the Newborn","volume":"46","author":"Maxwell","year":"2019","journal-title":"Clin. Perinatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1109\/TAFFC.2015.2462830","article-title":"The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset","volume":"7","author":"Aung","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_11","unstructured":"Martinez, B., and Valstar, M.F. (2016). Advances in Face Detection and Facial Image Analysis, Springer International Publishing."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.1109\/TCYB.2017.2662199","article-title":"Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification","volume":"52","author":"Rodriguez","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","first-page":"439","article-title":"Facial expression of pain: An evolutionary account","volume":"25","author":"Williams","year":"2002","journal-title":"Behav. Brain Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lopez-Martinez, D., and Picard, R. (2017, January 23\u201326). Multi-task neural networks for personalized pain recognition from physiological signals. Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), San Antonio, TX, USA.","DOI":"10.1109\/ACIIW.2017.8272611"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1016\/j.pain.2012.04.008","article-title":"Differentiating between heat pain intensities: The combined effect of multiple autonomic parameters","volume":"153","author":"Treister","year":"2012","journal-title":"Pain"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s10877-013-9487-9","article-title":"Monitoring the nociception level: A multi-parameter approach","volume":"27","author":"Kliger","year":"2013","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1093\/cercor\/bhr186","article-title":"Decoding an Individual\u2019s Sensitivity to Pain from the Multivariate Analysis of EEG Data","volume":"22","author":"Schulz","year":"2012","journal-title":"Cereb. Cortex"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lim, H., Kim, B., Noh, G.-J., and Yoo, S. (2019). A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal. Sensors, 19.","DOI":"10.3390\/s19020384"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0140-6736(14)61686-X","article-title":"Current concepts in management of pain in children in the emergency department","volume":"387","author":"Krauss","year":"2016","journal-title":"Lancet"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jnn.2005.04.003","article-title":"An exploration of nurses\u2019 knowledge of, and attitudes towards, pain recognition and management in neonates","volume":"11","author":"Brown","year":"2005","journal-title":"J. Neonatal Nurs."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rinella, S., Massimino, S., Fallica, P.G., Giacobbe, A., Donato, N., Coco, M., Neri, G., Parenti, R., Perciavalle, V., and Conoci, S. (2022). Emotion Recognition: Photoplethysmography and Electrocardiography in Comparison. Biosensors, 12.","DOI":"10.3390\/bios12100811"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1111\/j.1469-8986.1969.tb02850.x","article-title":"Correlation of Skin Potential and Skin Resistance Measures","volume":"5","author":"Gaviria","year":"1969","journal-title":"Psychophysiology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102770","DOI":"10.1109\/ACCESS.2022.3208905","article-title":"Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory\u2014An Example Based on the BioVid Heat Pain Database","volume":"10","author":"Bellmann","year":"2022","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3335","DOI":"10.1109\/JSEN.2020.3023656","article-title":"Automated Nociceptive Pain Assessment Using Physiological Signals and a Hybrid Deep Learning Network","volume":"21","author":"Subramaniam","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1109\/TAFFC.2019.2901673","article-title":"Feature Extraction and Selection for Emotion Recognition from Electrodermal Activity","volume":"12","author":"Shukla","year":"2021","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Das, P., Khasnobish, A., and Tibarewala, D.N. (2016, January 9\u201311). Emotion recognition employing ECG and GSR signals as markers of ANS. Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), Pune, India.","DOI":"10.1109\/CASP.2016.7746134"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Antoniou, E., Bozios, P., Christou, V., Tzimourta, K.D., Kalafatakis, K., Tsipouras, M.G., Giannakeas, N., and Tzallas, A.T. (2021). EEG-Based Eye Movement Recognition Using Brain\u2013Computer Interface and Random Forests. Sensors, 21.","DOI":"10.3390\/s21072339"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Su, R., Chen, X., Cao, S., and Zhang, X. (2016). Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors. Sensors, 16.","DOI":"10.3390\/s16010100"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Moeyersons, J., Morales, J., Seeuws, N., Van Hoof, C., Hermeling, E., Groenendaal, W., Willems, R., Van Huffel, S., and Varon, C. (2021). Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach. Sensors, 21.","DOI":"10.3390\/s21082613"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6815\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:23:00Z","timestamp":1760127780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,31]]},"references-count":32,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156815"],"URL":"https:\/\/doi.org\/10.3390\/s23156815","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,7,31]]}}}