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Building on this advancement, the present study aims to develop a neural network approach for predicting the anteroposterior component from the measured vertical component and to evaluate the impact of prediction errors on joint kinetics. A public data set comprising 9500 running steps was used to train, validate, and test the prediction model. The results demonstrated that a convolutional neural network can accurately predict the anteroposterior component of the GRF (relative root mean square error\u2009=\u20097.44%). While these anteroposterior-GRF prediction errors had only a minor impact on ankle moment calculations, estimates at the knee and hip were more substantially affected. These findings suggest that the proposed model may be effectively applied in conjunction with the size-adjustable instrumented track.<\/jats:p>","DOI":"10.1123\/jab.2025-0313","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:53:12Z","timestamp":1786038792000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Running Anteroposterior Ground Reaction Force From the Vertical Component Through a Deep Learning Approach"],"prefix":"10.1123","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7164-1659","authenticated-orcid":true,"given":"Remy","family":"Roinson","sequence":"first","affiliation":[{"name":"INRIA, University of Rennes, Rennes, France"},{"name":"Phyling, Orsay, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-0401","authenticated-orcid":true,"given":"Alexandre","family":"Karamanoukian","sequence":"additional","affiliation":[{"name":"Phyling, Orsay, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Philippe","family":"Boucher","sequence":"additional","affiliation":[{"name":"Phyling, Orsay, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7267-0236","authenticated-orcid":true,"given":"Nicolas","family":"Vignais","sequence":"additional","affiliation":[{"name":"INRIA, University of Rennes, Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"100","reference":[{"issue":"8","key":"r1","series-title":"Eur Spine J","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1007\/s00586-018-5569-1","article-title":"The use of gait analysis in the assessment of patients afflicted with spinal disorders","volume":"27","author":"Haddas R","year":"2018","unstructured":"Haddas R, Ju KL, Belanger T, Lieberman IH. 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