{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T21:13:16Z","timestamp":1778965996118,"version":"3.51.4"},"posted":{"date-parts":[[2025,12,9]]},"reference-count":0,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Musculoskeletal pain is an unpleasant sensation that affects nearly half of the European workers and leads to impaired work performance, representing a major occupational health concern. Although physiological-signal-based pain detection has been explored, existing research focuses predominantly on pain induced through thermal or electrical stimuli. However, the study of musculoskeletal pain requires comprehensive multimodal datasets with reliable annotations. This paper presents two novel datasets for musculoskeletal pain analysis comprising electrocardiogram (ECG), surface electromyography (sEMG) and kinematic data from the upper limbs, together with real-time pain annotations based on binary self-assessment (pain vs. no pain). One dataset contains 6 h and 4 min of data from 17 healthy participants with delayed-onset muscle soreness (DOMS) on the upper arm muscles and the other 1 h 6 min of data from 6 participants diagnosed with shoulder musculoskeletal disorders (MSDs). Both datasets were acquired while participants performed industrial tasks that required upper-limb movements capable of triggering musculoskeletal pain. These datasets may contribute to the development and testing of new pain detection algorithms and the study of the mechanisms underlying musculoskeletal pain.<\/jats:p>","DOI":"10.36227\/techrxiv.176524879.91910621\/v1","type":"posted-content","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T02:53:19Z","timestamp":1765248799000},"source":"Crossref","is-referenced-by-count":0,"title":["Collection: Multimodal datasets for analysing physiological and kinematic responses to shoulder pain and DOMS (PainMotion)"],"prefix":"10.36227","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3067-3881","authenticated-orcid":false,"given":"Diogo R.","family":"Martins","sequence":"first","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho"}]},{"given":"Sara M.","family":"Cerqueira","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho"}]},{"given":"Tiago P.","family":"Vieira","sequence":"additional","affiliation":[{"name":"Tiago Pinh\u00e3o -Terapias Partilhadas"}]},{"given":"Ana Maria A. C.","family":"Rocha","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Centre, Universidade do Minho"}]},{"given":"Alexandre Ferreira da","family":"Silva","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho"},{"name":"LABBELS -Associate Laboratory, University of Minho"}]},{"given":"Elazer R.","family":"Edelman","sequence":"additional","affiliation":[{"name":"Institute for Medical Engineering and Science, Massachusetts Institute of Technology"}]},{"given":"Mercedes","family":"Balcells","sequence":"additional","affiliation":[{"name":"Institute for Medical Engineering and Science, Massachusetts Institute of Technology"}]},{"given":"Cristina P.","family":"Santos","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho"},{"name":"LABBELS -Associate Laboratory, University of Minho"}]}],"member":"263","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.techrxiv.org\/doi\/pdf\/10.36227\/techrxiv.176524879.91910621\/v1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T20:26:52Z","timestamp":1778963212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techrxiv.org\/doi\/full\/10.36227\/techrxiv.176524879.91910621\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.36227\/techrxiv.176524879.91910621\/v1","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1109\/IEEEDATA.2026.3685288","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2025,12,9]]},"subtype":"preprint"}}