{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:12:25Z","timestamp":1770887545688,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T00:00:00Z","timestamp":1710633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Instituto Valenciano de Competitividad Empresarial (IVACE) and Valencian Regional Government (GVA)","award":["IMAMCA\/2024"],"award-info":[{"award-number":["IMAMCA\/2024"]}]},{"name":"Instituto Valenciano de Competitividad Empresarial (IVACE) and Valencian Regional Government (GVA)","award":["IMDEEA\/2024"],"award-info":[{"award-number":["IMDEEA\/2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In biomechanics, movement is typically recorded by tracking the trajectories of anatomical landmarks previously marked using passive instrumentation, which entails several inconveniences. To overcome these disadvantages, researchers are exploring different markerless methods, such as pose estimation networks, to capture movement with equivalent accuracy to marker-based photogrammetry. However, pose estimation models usually only provide joint centers, which are incomplete data for calculating joint angles in all anatomical axes. Recently, marker augmentation models based on deep learning have emerged. These models transform pose estimation data into complete anatomical data. Building on this concept, this study presents three marker augmentation models of varying complexity that were compared to a photogrammetry system. The errors in anatomical landmark positions and the derived joint angles were calculated, and a statistical analysis of the errors was performed to identify the factors that most influence their magnitude. The proposed Transformer model improved upon the errors reported in the literature, yielding position errors of less than 1.5 cm for anatomical landmarks and 4.4 degrees for all seven movements evaluated. Anthropometric data did not influence the errors, while anatomical landmarks and movement influenced position errors, and model, rotation axis, and movement influenced joint angle errors.<\/jats:p>","DOI":"10.3390\/s24061923","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T04:25:15Z","timestamp":1710735915000},"page":"1923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Deep Learning Model for Markerless Pose Estimation Based on Keypoint Augmentation: What Factors Influence Errors in Biomechanical Applications?"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1395-6795","authenticated-orcid":false,"given":"Ana V.","family":"Ruescas-Nicolau","sequence":"first","affiliation":[{"name":"Instituto de Biomec\u00e1nica-IBV, Universitat Polit\u00e8cnica de Val\u00e8ncia, Edifici 9C, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7141-0846","authenticated-orcid":false,"given":"Enrique","family":"Medina-Ripoll","sequence":"additional","affiliation":[{"name":"Instituto de Biomec\u00e1nica-IBV, Universitat Polit\u00e8cnica de Val\u00e8ncia, Edifici 9C, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4802-5851","authenticated-orcid":false,"given":"Helios","family":"de Rosario","sequence":"additional","affiliation":[{"name":"Instituto de Biomec\u00e1nica-IBV, Universitat Polit\u00e8cnica de Val\u00e8ncia, Edifici 9C, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8675-2729","authenticated-orcid":false,"given":"Joaqu\u00edn","family":"Sanchiz Navarro","sequence":"additional","affiliation":[{"name":"Instituto de Biomec\u00e1nica-IBV, Universitat Polit\u00e8cnica de Val\u00e8ncia, Edifici 9C, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9291-9780","authenticated-orcid":false,"given":"Eduardo","family":"Parrilla","sequence":"additional","affiliation":[{"name":"Instituto de Biomec\u00e1nica-IBV, Universitat Polit\u00e8cnica de Val\u00e8ncia, Edifici 9C, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8764-1470","authenticated-orcid":false,"given":"Mar\u00eda Carmen","family":"Juan Lizandra","sequence":"additional","affiliation":[{"name":"Instituto Universitario de Autom\u00e1tica e Inform\u00e1tica Industrial, Universitat Polit\u00e8cnica de Val\u00e8ncia, Edifici 1F, Cam\u00ed de Vera, s\/n, 46022 Valencia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1097\/00007632-199910150-00005","article-title":"The Quantification of Low Back Disorder Using Motion Measures. 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