{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T00:44:35Z","timestamp":1792975475409,"version":"4.5.0"},"reference-count":36,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100024255","name":"Institut TransMedTech","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100024255","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RRGPIN-2022-04217"],"award-info":[{"award-number":["RRGPIN-2022-04217"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010785","name":"Canada First Research Excellence Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020951","name":"Fonds de recherche du Qu\u00e9bec","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100020951","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000156","name":"Fonds de recherche du Quebec Sante","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000156","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","mcpdigitalhealth.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Mayo Clinic Proceedings: Digital Health"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1016\/j.mcpdig.2024.11.004","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T03:24:05Z","timestamp":1732677845000},"page":"100183","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors"],"prefix":"10.1016","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2275-8678","authenticated-orcid":false,"given":"Vaibhav R.","family":"Shah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe C.","family":"Dixon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"23","key":"10.1016\/j.mcpdig.2024.11.004_bib1","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.3390\/s19235227","article-title":"Estimating biomechanical time-series with wearable sensors: a systematic review of machine learning techniques","volume":"19","author":"Gurchiek","year":"2019","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2018.09.009","article-title":"Machine learning in human movement biomechanics: best practices, common pitfalls, and new opportunities","volume":"81","author":"Halilaj","year":"2018","journal-title":"J\u00a0Biomech"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib3","doi-asserted-by":"crossref","first-page":"817","DOI":"10.3389\/fpsyg.2017.00817","article-title":"Inertial sensors to assess gait quality in patients with neurological disorders: a systematic review of technical and analytical challenges","volume":"8","author":"Vienne","year":"2017","journal-title":"Front Psychol"},{"issue":"4","key":"10.1016\/j.mcpdig.2024.11.004_bib4","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1123\/jsr.2016-0125","article-title":"A\u00a0comparison of both motorized and nonmotorized treadmill gait kinematics to overground locomotion","volume":"27","author":"Fullenkamp","year":"2018","journal-title":"J\u00a0Sport Rehabil"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib5","doi-asserted-by":"crossref","first-page":"90","DOI":"10.3389\/fphys.2020.00090","article-title":"Wearable inertial measurement units for assessing gait in real-world environments","volume":"11","author":"Renggli","year":"2020","journal-title":"Front Physiol"},{"issue":"1","key":"10.1016\/j.mcpdig.2024.11.004_bib6","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1038\/s41598-020-59002-6","article-title":"Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy","volume":"10","author":"Carcreff","year":"2020","journal-title":"Sci Rep"},{"issue":"7","key":"10.1016\/j.mcpdig.2024.11.004_bib7","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.3390\/s19071597","article-title":"Exploring the role of wearable technology in sport kinematics and kinetics: a systematic review","volume":"19","author":"Adesida","year":"2019","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.mcpdig.2024.11.004_bib8","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/JSEN.2020.3019016","article-title":"Wearable sensors for real-time kinematics analysis in sports: a review","volume":"21","author":"Rana","year":"2020","journal-title":"IEEE Sens J"},{"issue":"1","key":"10.1016\/j.mcpdig.2024.11.004_bib9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12984-022-01001-x","article-title":"OpenSense: an open-source toolbox for Inertial-Measurement-Unit-based measurement of lower extremity kinematics over long durations","volume":"19","author":"Al Borno","year":"2022","journal-title":"J\u00a0Neuroeng Rehabil"},{"issue":"3","key":"10.1016\/j.mcpdig.2024.11.004_bib10","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/s20030673","article-title":"Inertial sensor-based lower limb joint kinematics: a methodological systematic review","volume":"20","author":"Weygers","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib11","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.jbiomech.2018.12.023","article-title":"Assessment of the measurement accuracy of inertial sensors during different tasks of daily living","volume":"84","author":"Mundt","year":"2019","journal-title":"J\u00a0Biomech"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.jbiomech.2017.03.015","article-title":"Measuring joint kinematics of treadmill walking and running: comparison between an inertial sensor based system and a camera-based system","volume":"57","author":"N\u00fcesch","year":"2017","journal-title":"J\u00a0Biomech"},{"issue":"4","key":"10.1016\/j.mcpdig.2024.11.004_bib13","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s12283-012-0103-x","article-title":"Impact of hockey skate design on ankle motion and force production","volume":"15","author":"Robert-Lachaine","year":"2012","journal-title":"Sports Eng"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib14","article-title":"Xsens MVN: Consistent Tracking of Human Motion Using Inertial Sensing","author":"Schepers","year":"2018","journal-title":"XSens Technologies"},{"issue":"13","key":"10.1016\/j.mcpdig.2024.11.004_bib15","doi-asserted-by":"crossref","first-page":"4535","DOI":"10.3390\/s21134535","article-title":"A\u00a0comparison of three neural network approaches for estimating joint angles and moments from inertial measurement units","volume":"21","author":"Mundt","year":"2021","journal-title":"Sensors (Basel)"},{"issue":"1","key":"10.1016\/j.mcpdig.2024.11.004_bib16","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.gaitpost.2007.11.001","article-title":"Predicting lower limb joint kinematics using wearable motion sensors","volume":"28","author":"Findlow","year":"2008","journal-title":"Gait Posture"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib17","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2018.00218","article-title":"Estimation of vertical ground reaction forces and sagittal knee kinematics during running using three inertial sensors","volume":"9","author":"Wouda","year":"2018","journal-title":"Front Physiol"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib18","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2020.00041","article-title":"Estimation of gait mechanics based on simulated and measured IMU data using an artificial neural network","volume":"8","author":"Mundt","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib19","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2020.00604","article-title":"CNN-based estimation of sagittal plane walking and running biomechanics from measured and simulated inertial sensor data","volume":"8","author":"Dorschky","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbiomech.2021.110552","article-title":"Real-time conversion of inertial measurement unit data to ankle joint angles using deep neural networks","volume":"125","author":"Senanayake","year":"2021","journal-title":"J\u00a0Biomech"},{"issue":"1","key":"10.1016\/j.mcpdig.2024.11.004_bib21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3390\/s22010053","article-title":"Prediction of lower extremity multi-joint angles during overground walking by using a single IMU with a low frequency based on an LSTM recurrent neural network","volume":"22","author":"Sung","year":"2021","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.mcpdig.2024.11.004_bib22","doi-asserted-by":"crossref","DOI":"10.3390\/s22020446","article-title":"Predicting knee joint kinematics from wearable sensor data in people with knee osteoarthritis and clinical considerations for future machine learning models","volume":"22","author":"Tan","year":"2022","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbiomech.2021.110229","article-title":"Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework","volume":"116","author":"Rapp","year":"2021","journal-title":"J\u00a0Biomech"},{"issue":"17","key":"10.1016\/j.mcpdig.2024.11.004_bib24","doi-asserted-by":"crossref","first-page":"5876","DOI":"10.3390\/s21175876","article-title":"The use of synthetic IMU signals in the training of deep learning models significantly improves the accuracy of joint kinematic predictions","volume":"21","author":"Renani","year":"2021","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib25","series-title":"Deep Learning with Python","author":"Chollet","year":"2017"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib26","series-title":"Encyclopedia of Biometrics","first-page":"899","article-title":"LDA (linear discriminant analysis)","author":"Teng","year":"2009"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib27","series-title":"Functional Data Analysis","author":"Ramsay","year":"2005"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib28","author":"Meert"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib29","series-title":"wannesm\/dtaidistance: v2.3.5","author":"wannesm","year":"2022"},{"issue":"3","key":"10.1016\/j.mcpdig.2024.11.004_bib30","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1214\/aos\/1069362747","article-title":"Alignment of curves by dynamic time warping","volume":"25","author":"Wang","year":"1997","journal-title":"Ann Stat"},{"issue":"3","key":"10.1016\/j.mcpdig.2024.11.004_bib31","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1002\/jor.1100080310","article-title":"Measurement of lower extremity kinematics during level walking","volume":"8","author":"Kadaba","year":"1990","journal-title":"J\u00a0Orthop Res"},{"issue":"3","key":"10.1016\/j.mcpdig.2024.11.004_bib32","first-page":"318","article-title":"Estimate of the optimum cutoff frequency for the butterworth low-pass digital filter","volume":"15","author":"Yu","year":"1999","journal-title":"J\u00a0Appl Biomech"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib33","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.gaitpost.2021.09.176","article-title":"The impact of outdoor walking surfaces on lower-limb coordination and variability during gait in healthy adults","volume":"91","author":"Ippersiel","year":"2022","journal-title":"Gait Posture"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbiomech.2022.111159","article-title":"Generalizability of deep learning models for predicting outdoor irregular walking surfaces","volume":"139","author":"Shah","year":"2022","journal-title":"J\u00a0Biomech"},{"issue":"10","key":"10.1016\/j.mcpdig.2024.11.004_bib37","doi-asserted-by":"crossref","first-page":"136","DOI":"10.3390\/data7100136","article-title":"Full-body mobility data to validate inertial measurement unit algorithms in healthy and neurological cohorts","volume":"7","author":"Warmerdam","year":"2022","journal-title":"Data"},{"key":"10.1016\/j.mcpdig.2024.11.004_bib38","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.medengphy.2020.10.001","article-title":"Prediction of ground reaction force and joint moments based on optical motion capture data during gait","volume":"86","author":"Mundt","year":"2020","journal-title":"Med Eng Phys"}],"container-title":["Mayo Clinic Proceedings: Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2949761224001135?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2949761224001135?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T12:57:16Z","timestamp":1778936236000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2949761224001135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["S2949761224001135"],"URL":"https:\/\/doi.org\/10.1016\/j.mcpdig.2024.11.004","relation":{},"ISSN":["2949-7612"],"issn-type":[{"value":"2949-7612","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors","name":"articletitle","label":"Article Title"},{"value":"Mayo Clinic Proceedings: Digital Health","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mcpdig.2024.11.004","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Inc on behalf of Mayo Foundation for Medical Education and Research.","name":"copyright","label":"Copyright"}],"article-number":"100183"}}