{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T22:19:29Z","timestamp":1769465969846,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["2022.09534.BD"],"award-info":[{"award-number":["2022.09534.BD"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>This study introduces a machine-learning-based approach for identifying \u201cpain signatures\u201d using electromyography data from volunteers undergoing acute pain. Leveraging the XGBoost algorithm, our method analyzes electromyography features (variance, mean absolute deviation, integral, peak, and entropy) to classify muscle contractions as painful or non-painful. Fifteen participants performed controlled elbow flexion tasks under three conditions: during painful and painless conditions. The results revealed that electromyographic peak and integral activity were key predictors of pain states, with the model achieving 73% sensitivity in distinguishing painful from painless conditions. Interestingly, placebo-induced responses with less intense pain exhibited muscular adaptations similar to, but less extensive than, those observed under actual pain. These findings underscore the potential of machine learning to enhance pain assessment by providing a non-verbal, objective method for analyzing neuromuscular adaptations, paving the way for personalized pain management and more accurate monitoring of musculoskeletal health.<\/jats:p>","DOI":"10.3390\/bioengineering13020141","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T11:14:07Z","timestamp":1769426047000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can Machines Identify Pain Effects? A Machine Learning Proof of Concept to Identify EMG Pain Signature"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4814-9261","authenticated-orcid":false,"given":"Klaus","family":"Becker","sequence":"first","affiliation":[{"name":"Porto Biomechanics Laboratory, University of Porto, 4200-450 Porto, Portugal"},{"name":"Center of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6765-6475","authenticated-orcid":false,"given":"Franciele","family":"Parolini","sequence":"additional","affiliation":[{"name":"Porto Biomechanics Laboratory, University of Porto, 4200-450 Porto, Portugal"},{"name":"Center for Rehabilitation Research (CIR), ESS, Polytechnic of Porto, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3922-7598","authenticated-orcid":false,"given":"Venicius de Paula","family":"Silva","sequence":"additional","affiliation":[{"name":"Laboratory of Physical Activity Sciences, School of Arts, Sciences, and Humanities, University of S\u00e3o Paulo, S\u00e3o Paulo 03828-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4109-2939","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Vilas-Boas","sequence":"additional","affiliation":[{"name":"Porto Biomechanics Laboratory, University of Porto, 4200-450 Porto, Portugal"},{"name":"Center of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7787-4860","authenticated-orcid":false,"given":"Thomas","family":"Graven-Nielsen","sequence":"additional","affiliation":[{"name":"Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Faculty of Medicine, Aalborg University, 9260 Aalborg, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4528-4644","authenticated-orcid":false,"given":"Ulysses","family":"Ervilha","sequence":"additional","affiliation":[{"name":"Center of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal"},{"name":"Laboratory of Physical Activity Sciences, School of Arts, Sciences, and Humanities, University of S\u00e3o Paulo, S\u00e3o Paulo 03828-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4382-0159","authenticated-orcid":false,"given":"M\u00e1rcio","family":"Goethel","sequence":"additional","affiliation":[{"name":"Porto Biomechanics Laboratory, University of Porto, 4200-450 Porto, Portugal"},{"name":"Center of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1002\/j.1532-2149.2012.00186.x","article-title":"Motor consequences of experimentally induced limb pain: A systematic review","volume":"17","author":"Bank","year":"2013","journal-title":"Eur. J. Pain"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1179\/016164103101201283","article-title":"Pain-related modulation of the human motor cortex","volume":"25","author":"Farina","year":"2003","journal-title":"Neurol. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1007\/s11682-021-00621-6","article-title":"Pain\u2019s Adverse Impact on Training-Induced Performance and Neuroplasticity: A Systematic Review","volume":"16","author":"Stanisic","year":"2022","journal-title":"Brain Imaging Behav."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1053\/apmr.2001.19022","article-title":"Delayed onset of electromyographic activity of vastus medialis obliquus relative to vastus lateralis in subjects with patellofemoral pain syndrome","volume":"82","author":"Cowan","year":"2001","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.jpain.2008.06.012","article-title":"Changes in Motor Unit Firing Rate in Synergist Muscles Cannot Explain the Maintenance of Force During Constant Force Painful Contractions","volume":"9","author":"Hodges","year":"2008","journal-title":"J. Pain"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Aeles, J., Horst, F., Lapuschkin, S., Lacourpaille, L., and Hug, F. (2020). Revealing the unique features of each individual\u2019s muscle activation signatures. bioRxiv.","DOI":"10.1101\/2020.07.23.217034"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1152\/jn.00347.2016","article-title":"The compensatory interaction between motor unit firing behavior and muscle force during fatigue","volume":"116","author":"Contessa","year":"2016","journal-title":"J. Neurophysiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1002\/j.1532-2149.2013.00286.x","article-title":"New insight into motor adaptation to pain revealed by a combination of modelling and empirical approaches","volume":"17","author":"Hodges","year":"2013","journal-title":"Eur. J. Pain"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1152\/jn.00557.2014","article-title":"Effect of acute noxious stimulation to the leg or back on muscle synergies during walking","volume":"113","author":"Hodges","year":"2015","journal-title":"J. Neurophysiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s00421-023-05193-5","article-title":"Adaptability of the load sharing between the longissimus and components of the multifidus muscle during isometric trunk extension in healthy individuals","volume":"123","author":"Tier","year":"2023","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1249\/JES.0000000000000122","article-title":"Muscle Coordination and the Development of Musculoskeletal Disorders","volume":"45","author":"Hug","year":"2017","journal-title":"Exerc. Sport Sci. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.gaitpost.2016.06.025","article-title":"Pain catastrophizing and trunk muscle activation during walking in patients with chronic low back pain","volume":"49","author":"Pakzad","year":"2016","journal-title":"Gait Posture"},{"key":"ref_13","first-page":"MR000055","article-title":"Impact of active placebo controls on estimated drug effects in randomised trials: A systematic review of trials with both active placebo and standard placebo","volume":"2023","author":"Laursen","year":"2023","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/S0140-6736(09)61706-2","article-title":"Biological, clinical, and ethical advances of placebo effects","volume":"375","author":"Finniss","year":"2010","journal-title":"Lancet"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rani, G.J., and Hashmi, M.F. (2023, January 29\u201330). Electromyography (EMG) Signal based Knee Abnormality Prediction using XGBoost Machine Learning Algorithm. Proceedings of the 2023 IEEE 2nd International Conference on Industrial Electronics: Developments & Applications (ICIDeA), Imphal, India.","DOI":"10.1109\/ICIDeA59866.2023.10295245"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1007\/s00421-004-1083-8","article-title":"Effect of load level and muscle pain intensity on the motor control of elbow-flexion movements","volume":"92","author":"Ervilha","year":"2004","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3103\/S0735272719010047","article-title":"Fingers Movements Control System Based on Artificial Neural Network Model","volume":"62","author":"Vonsevych","year":"2019","journal-title":"Radioelectron. Commun. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1631\/jzus.2006.B0844","article-title":"Characterization of surface EMG signals using improved approximate entropy","volume":"7","author":"Chen","year":"2006","journal-title":"J. Zhejiang Univ. Sci. B"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/JAS.2021.1003865","article-title":"Deep Learning for EMG-based Human-Machine Interaction: A Review","volume":"8","author":"Xiong","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Moustakidis, S., Plakias, S., Kokkotis, C., Tsatalas, T., and Tsaopoulos, D. (2023). Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics. Futur. Internet, 15.","DOI":"10.3390\/fi15050174"},{"key":"ref_22","unstructured":"Cohen, J. (1987). Statistical Power Analysis for the Behavioral Sciences (Revised Edition), Lawrence Erlbaum Associates."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"48349","DOI":"10.1109\/ACCESS.2024.3384359","article-title":"PainMeter: Automatic Assessment of Pain Intensity Levels From Multiple Physiological Signals Using Machine Learning","volume":"12","author":"Albahdal","year":"2024","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s11760-022-02315-y","article-title":"sEMG-based deep learning framework for the automatic detection of knee abnormality","volume":"17","author":"Vijayvargiya","year":"2023","journal-title":"Signal Image Video Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Becker, K., Goethel, M., Fonseca, P., Vilas-Boas, J.P., and Ervilha, U. (2022). The Strategy of the Brain to Maintain the Force Production in Painful Contractions\u2014A Motor Units Pool Reorganization. Cells, 11.","DOI":"10.3390\/cells11203299"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"S90","DOI":"10.1016\/j.pain.2010.10.020","article-title":"Moving differently in pain: A new theory to explain the adaptation to pain","volume":"152","author":"Hodges","year":"2011","journal-title":"Pain"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1037\/0735-7044.120.2.474","article-title":"Reduced variability of postural strategy prevents normalization of motor changes induced by back pain: A risk factor for chronic trouble?","volume":"120","author":"Moseley","year":"2006","journal-title":"Behav. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1176\/appi.ajp.159.5.728","article-title":"The Functional Neuroanatomy of the Placebo Effect","volume":"159","author":"Mayberg","year":"2002","journal-title":"Am. J. Psychiatry"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1146\/annurev.psych.59.113006.095941","article-title":"A Comprehensive Review of the Placebo Effect: Recent Advances and Current Thought","volume":"59","author":"Price","year":"2008","journal-title":"Annu. Rev. Psychol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10390","DOI":"10.1523\/JNEUROSCI.3458-05.2005","article-title":"Neurobiological mechanisms of the placebo effect","volume":"25","author":"Benedetti","year":"2005","journal-title":"J. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.neuron.2007.07.012","article-title":"The Cerebral Signature for Pain Perception and Its Modulation","volume":"55","author":"Tracey","year":"2007","journal-title":"Neuron"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.ejpain.2004.11.001","article-title":"Human brain mechanisms of pain perception and regulation in health and disease","volume":"9","author":"Apkarian","year":"2005","journal-title":"Eur. J. Pain"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1054\/jpai.2001.19951","article-title":"Increased Muscle Activity Flexor Withdrawal Reflex","volume":"2","author":"Sterling","year":"2001","journal-title":"J. Pain"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11926-007-0080-4","article-title":"Neural and muscular factors associated with motor impairment in neck pain","volume":"9","author":"Falla","year":"2007","journal-title":"Curr. Rheumatol. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1093\/brain\/awn116","article-title":"Placebo effects: Clinical aspects and neurobiology","volume":"131","author":"Oken","year":"2008","journal-title":"Brain"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1097\/PSY.0000000000001194","article-title":"Learned Nocebo Effects on Cutaneous Sensations of Pain and Itch: A Systematic Review and Meta-analysis of Experimental Behavioral Studies on Healthy Humans","volume":"85","author":"Thomaidou","year":"2023","journal-title":"Psychosom. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e00464","DOI":"10.1016\/j.neurot.2024.e00464","article-title":"Neuroplasticity in the transition from acute to chronic pain","volume":"21","author":"Song","year":"2024","journal-title":"Neurotherapeutics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1002\/ejp.1140","article-title":"Assessment and manifestation of central sensitisation across different chronic pain conditions","volume":"22","author":"Morlion","year":"2018","journal-title":"Eur. J. Pain"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Care, P., Haas, R., Gorelik, A., Busija, L., Connor, D.O., Pearce, C., Mazza, D., and Buchbinder, R. (2023). Prevalence and characteristics of musculoskeletal complaints in primary care: An analysis from the population level and analysis reporting (POLAR) database. BMC Prim. Care, 24.","DOI":"10.1186\/s12875-023-01976-z"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/2\/141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T11:50:38Z","timestamp":1769428238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/2\/141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,26]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["bioengineering13020141"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13020141","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,26]]}}}