{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T21:25:07Z","timestamp":1791321907120,"version":"4.3.1"},"reference-count":83,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007834","name":"Natural Science Foundation of Ningbo Municipality","doi-asserted-by":"publisher","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007834","name":"Natural Science Foundation of Ningbo Municipality","doi-asserted-by":"publisher","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2023C03197"],"award-info":[{"award-number":["2023C03197"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC3607305"],"award-info":[{"award-number":["2024YFC3607305"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Gait &amp; Posture"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.gaitpost.2025.110083","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T23:35:18Z","timestamp":1765668918000},"page":"110083","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Artificial intelligence in lower limb joint moment prediction during typically developed gait: A systematic review and multilevel random-effects meta-analysis"],"prefix":"10.1016","volume":"125","author":[{"given":"Jianqi","family":"Pan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4345-8201","authenticated-orcid":false,"given":"Zixiang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanyi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diwei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien S.","family":"Baker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaodong","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.gaitpost.2025.110083_bib1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1097\/00003086-198305000-00021","article-title":"Energy generation and absorption at the ankle and knee during fast, natural, and slow cadences","author":"Winter","year":"1983","journal-title":"Clin. Orthop. Relat. Res."},{"key":"10.1016\/j.gaitpost.2025.110083_bib2","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.gaitpost.2012.09.006","article-title":"Secondary gait deviations in patients with and without neurological involvement: a systematic review","volume":"37","author":"Schmid","year":"2013","journal-title":"Gait Posture"},{"key":"10.1016\/j.gaitpost.2025.110083_bib3","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/S1047-9651(18)30666-1","article-title":"Biomechanics of normal gait","volume":"3","author":"Lehmann","year":"1992","journal-title":"Phys. Med. Rehabil. Clin. North Am."},{"key":"10.1016\/j.gaitpost.2025.110083_bib4","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1109\/TIP.2004.832865","article-title":"Identification of humans using gait","volume":"13","author":"Kale","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.gaitpost.2025.110083_bib5","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.gaitpost.2023.06.014","article-title":"The impact of three-dimensional gait analysis in adults with pathological gait on management recommendations","volume":"105","author":"McGrath","year":"2023","journal-title":"Gait Posture"},{"key":"10.1016\/j.gaitpost.2025.110083_bib6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ejpn.2022.11.007","article-title":"The impact of instrumented gait analysis on decision-making in the interprofessional management of cerebral palsy: a scoping review","volume":"42","author":"Hebda-Boon","year":"2023","journal-title":"Eur. J. Paediatr. Neurol."},{"key":"10.1016\/j.gaitpost.2025.110083_bib7","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1080\/01942638.2022.2037808","article-title":"Clinicians\u2019 experiences of instrumented gait analysis in management of patients with cerebral palsy: a qualitative study","volume":"42","author":"Hebda-Boon","year":"2022","journal-title":"Phys. Occup. Ther. Pediatr."},{"key":"10.1016\/j.gaitpost.2025.110083_bib8","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.gaitpost.2020.05.031","article-title":"Clinical efficacy of instrumented gait analysis: systematic review 2020 update","volume":"80","author":"Wren","year":"2020","journal-title":"Gait Posture"},{"key":"10.1016\/j.gaitpost.2025.110083_bib9","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.gaitpost.2011.11.030","article-title":"Gait symmetry and velocity differ in their relationship to age","volume":"35","author":"Patterson","year":"2012","journal-title":"Gait Posture"},{"key":"10.1016\/j.gaitpost.2025.110083_bib10","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.gaitpost.2011.06.002","article-title":"Influence of gait analysis on decision-making for lower extremity orthopaedic surgery: baseline data from a randomized controlled trial","volume":"34","author":"Wren","year":"2011","journal-title":"Gait Posture"},{"key":"10.1016\/j.gaitpost.2025.110083_bib11","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.physio.2013.03.001","article-title":"Affordable clinical gait analysis: an assessment of the marker tracking accuracy of a new low-cost optical 3D motion analysis system","volume":"99","author":"Carse","year":"2013","journal-title":"Physiotherapy"},{"key":"10.1016\/j.gaitpost.2025.110083_bib12","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.gaitpost.2003.09.011","article-title":"Measurement and management of errors in quantitative gait data","volume":"20","author":"Schwartz","year":"2004","journal-title":"Gait Posture"},{"key":"10.1016\/j.gaitpost.2025.110083_bib13","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.procs.2015.03.116","article-title":"Passive marker based optical system for gait kinematics for lower extremity","volume":"45","author":"Prakash","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.gaitpost.2025.110083_bib14","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s12938-017-0396-x","article-title":"Methodological factors affecting joint moments estimation in clinical gait analysis: a systematic review","volume":"16","author":"Camomilla","year":"2017","journal-title":"Biomed. Eng. OnLine"},{"key":"10.1016\/j.gaitpost.2025.110083_bib15","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1016\/j.jbiomech.2004.02.047","article-title":"Quantification of human motion: gait analysis\u2014benefits and limitations to its application to clinical problems","volume":"37","author":"Simon","year":"2004","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib16","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/1743-0003-3-4","article-title":"Gait analysis methods in rehabilitation","volume":"3","author":"Baker","year":"2006","journal-title":"J. Neuroeng. Rehabil."},{"key":"10.1016\/j.gaitpost.2025.110083_bib17","series-title":"Handbook of Human Motion","first-page":"625","article-title":"Interpreting joint moments and powers in gait","author":"Sloot","year":"2018"},{"key":"10.1016\/j.gaitpost.2025.110083_bib18","first-page":"26","article-title":"Recent developments and challenges of lower extremity exoskeletons","volume":"5","author":"Chen","year":"2016","journal-title":"J. Orthop. Transl."},{"key":"10.1016\/j.gaitpost.2025.110083_bib19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2018.04.017","article-title":"Ankle and foot power in gait analysis: implications for science, technology and clinical assessment","volume":"75","author":"Zelik","year":"2018","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib20","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11517-019-02061-3","article-title":"Prediction of lower limb joint angles and moments during gait using artificial neural networks","volume":"58","author":"Mundt","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.gaitpost.2025.110083_bib21","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1007\/s11517-018-1802-7","article-title":"Predicting athlete ground reaction forces and moments from motion capture","volume":"56","author":"Johnson","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.gaitpost.2025.110083_bib22","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.jbiomech.2013.07.036","article-title":"Prediction of ground reaction forces during gait based on kinematics and a neural network model","volume":"46","author":"Oh","year":"2013","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib23","first-page":"383","article-title":"Artificial intelligence approach in biomechanics of gait and sport: a systematic literature review","volume":"13","author":"Molavian","year":"2023","journal-title":"J. Biomed. Phys. Eng."},{"key":"10.1016\/j.gaitpost.2025.110083_bib24","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1109\/TNSRE.2017.2736939","article-title":"An automated classification of pathological gait using unobtrusive sensing technology","volume":"25","author":"Dolatabadi","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.gaitpost.2025.110083_bib25","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/BF03178892","article-title":"Neural networks for detection and classification of walking pattern changes due to ageing","volume":"29","author":"Begg","year":"2006","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"10.1016\/j.gaitpost.2025.110083_bib26","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TNSRE.2018.2864119","article-title":"Spatio\u2013spectral representation learning for electroencephalographic gait-pattern classification","volume":"26","author":"Goh","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.gaitpost.2025.110083_bib27","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/THMS.2017.2759807","article-title":"ISR-AIWALKER: robotic walker for intuitive and safe mobility assistance and gait analysis","volume":"47","author":"Paulo","year":"2017","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"10.1016\/j.gaitpost.2025.110083_bib28","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2023.1215770","article-title":"Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors","volume":"11","author":"Altai","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"key":"10.1016\/j.gaitpost.2025.110083_bib29","doi-asserted-by":"crossref","first-page":"2829","DOI":"10.1109\/JBHI.2023.3262164","article-title":"Estimation of lower extremity joint moments and 3D ground reaction forces using IMU sensors in multiple walking conditions: a deep learning approach","volume":"27","author":"Hossain","year":"2023","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.gaitpost.2025.110083_bib30","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1080\/03091902.2020.1822940","article-title":"Role of machine learning in gait analysis: a review","volume":"44","author":"Khera","year":"2020","journal-title":"J. Med. Eng. Technol."},{"key":"10.1016\/j.gaitpost.2025.110083_bib31","doi-asserted-by":"crossref","first-page":"264","DOI":"10.3390\/technologies12120264","article-title":"Performance evaluation of various deep learning models in gait recognition using the CASIA-B dataset","volume":"12","author":"Aman","year":"2024","journal-title":"Technologies"},{"key":"10.1016\/j.gaitpost.2025.110083_bib32","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1038\/s41598-023-31906-z","article-title":"A comparison of machine learning models\u2019 accuracy in predicting lower-limb joints\u2019 kinematics, kinetics, and muscle forces from wearable sensors","volume":"13","author":"Moghadam","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.gaitpost.2025.110083_bib33","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.gaitpost.2025.110083_bib34","doi-asserted-by":"crossref","DOI":"10.1080\/08839514.2019.1637138","article-title":"Comparison of performance of data imputation methods for numeric dataset","author":"Jadhav","year":"2019","journal-title":"Appl. Artif. Intell."},{"key":"10.1016\/j.gaitpost.2025.110083_bib35","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.3390\/s23063331","article-title":"EMG-based estimation of lower limb joint angles and moments using long short-term memory network","volume":"23","author":"Truong","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.gaitpost.2025.110083_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105372","article-title":"Deep-learning model for the prediction of lower-limb joint moments using single inertial measurement unit during different locomotive activities","volume":"86","author":"Liang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.gaitpost.2025.110083_bib37","doi-asserted-by":"crossref","first-page":"4535","DOI":"10.3390\/s21134535","article-title":"A comparison of three neural network approaches for estimating joint angles and moments from inertial measurement units","volume":"21","author":"Mundt","year":"2021","journal-title":"Sens"},{"key":"10.1016\/j.gaitpost.2025.110083_bib38","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TNSRE.2019.2953588","article-title":"Prediction of ankle dorsiflexion moment by combined ultrasound sonography and electromyography","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.gaitpost.2025.110083_bib39","doi-asserted-by":"crossref","first-page":"9575","DOI":"10.1109\/JSEN.2019.2928777","article-title":"Deep learning for monitoring of human gait: a review","volume":"19","author":"Alharthi","year":"2019","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.gaitpost.2025.110083_bib40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-016-9514-6","article-title":"Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges","volume":"49","author":"Prakash","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.gaitpost.2025.110083_bib41","series-title":"Advances in Genetics","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0065-2660(07)00413-0","article-title":"Meta-analysis methods","author":"Trikalinos","year":"2008"},{"key":"10.1016\/j.gaitpost.2025.110083_bib42","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s40279-019-01183-w","article-title":"Effects of interrupting prolonged sitting with physical activity breaks on blood glucose, insulin and triacylglycerol measures: a systematic review and meta-analysis","volume":"50","author":"Loh","year":"2020","journal-title":"Sports Med."},{"key":"10.1016\/j.gaitpost.2025.110083_bib43","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s40279-022-01766-0","article-title":"With great power comes great responsibility: common errors in meta-analyses and meta-regressions in strength & conditioning research","volume":"53","author":"Kadlec","year":"2023","journal-title":"Sports Med."},{"key":"10.1016\/j.gaitpost.2025.110083_bib44","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.ijsu.2010.02.007","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"8","author":"Moher","year":"2010","journal-title":"Int. J. Surg."},{"key":"10.1016\/j.gaitpost.2025.110083_bib45","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1186\/s12913-014-0579-0","article-title":"PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews","volume":"14","author":"Methley","year":"2014","journal-title":"BMC Health Serv. Res."},{"key":"10.1016\/j.gaitpost.2025.110083_bib46","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"10.1016\/j.gaitpost.2025.110083_bib47","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1111\/2041-210X.14377","article-title":"Robust point and variance estimation for meta-analyses with selective reporting and dependent effect sizes","volume":"15","author":"Yang","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.gaitpost.2025.110083_bib48","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1002\/jrsm.11","article-title":"Outlier and influence diagnostics for meta-analysis","volume":"1","author":"Viechtbauer","year":"2010","journal-title":"Res. Synth. Method"},{"key":"10.1016\/j.gaitpost.2025.110083_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.jml.2019.104038","article-title":"How to capitalize on a priori contrasts in linear (mixed) models: a tutorial","volume":"110","author":"Schad","year":"2020","journal-title":"J. Mem. Lang."},{"key":"10.1016\/j.gaitpost.2025.110083_bib50","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1109\/JBHI.2024.3512546","article-title":"Estimation of ankle joint moment from plantar pressure through an optimized sensor layout using genetic algorithm and deep forest regression","volume":"29","author":"Gong","year":"2025","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.gaitpost.2025.110083_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108016","article-title":"Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors","volume":"170","author":"Xiang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.gaitpost.2025.110083_bib52","first-page":"1","article-title":"Exploring the contribution of joint angles and sEMG signals on joint torque prediction accuracy using LSTM-based deep learning techniques","author":"Kaya","year":"2024","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"10.1016\/j.gaitpost.2025.110083_bib53","doi-asserted-by":"crossref","first-page":"8795","DOI":"10.3390\/app14198795","article-title":"Transferable deep learning models for accurate ankle joint moment estimation during gait using electromyography","volume":"14","author":"Ali","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.gaitpost.2025.110083_bib54","doi-asserted-by":"crossref","first-page":"9576","DOI":"10.3390\/s23239576","article-title":"Lower limb joint torque prediction using long short-term memory network and gaussian process regression","volume":"23","author":"Wang","year":"2023","journal-title":"Sens"},{"key":"10.1016\/j.gaitpost.2025.110083_bib55","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1109\/TII.2022.3189648","article-title":"IMU and smartphone camera fusion for knee adduction and knee flexion moment estimation during walking","volume":"19","author":"Tan","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.gaitpost.2025.110083_bib56","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbiomech.2023.111668","article-title":"Machine learning-based prediction of joint moments based on kinematics in patients with cerebral palsy","volume":"155","author":"Ozates","year":"2023","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10255842.2022.2044028","article-title":"Developing a method for quantifying hip joint angles and moments during walking using neural networks and wearables","volume":"26","author":"McCabe","year":"2023","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"10.1016\/j.gaitpost.2025.110083_bib58","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/acd5f0","article-title":"Continuous estimation of kinetic variables while walking using an accelerometer and artificial neural networks","volume":"34","author":"Mantashloo","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.gaitpost.2025.110083_bib59","doi-asserted-by":"crossref","first-page":"154","DOI":"10.3390\/machines9080154","article-title":"Kinematics, speed, and anthropometry-based ankle joint torque estimation: a deep learning regression approach","volume":"9","author":"Moreira","year":"2021","journal-title":"Machines"},{"key":"10.1016\/j.gaitpost.2025.110083_bib60","doi-asserted-by":"crossref","first-page":"6277","DOI":"10.3390\/s20216277","article-title":"Estimation of three-dimensional lower limb kinetics data during walking using machine learning from a single IMU attached to the sacrum","volume":"20","author":"Lee","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.gaitpost.2025.110083_bib61","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.jbiomech.2018.01.034","article-title":"Classifying running speed conditions using a single wearable sensor: optimal segmentation and feature extraction methods","volume":"71","author":"Benson","year":"2018","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib62","article-title":"A marker registration method to improve joint angles computed by constrained inverse kinematics","volume":"16","author":"Dunne","year":"2021","journal-title":"PLoS One"},{"key":"10.1016\/j.gaitpost.2025.110083_bib63","doi-asserted-by":"crossref","DOI":"10.1115\/1.4034708","article-title":"How different marker sets affect joint angles in inverse kinematics framework","volume":"139","author":"Mantovani","year":"2017","journal-title":"J. Biomech. Eng."},{"key":"10.1016\/j.gaitpost.2025.110083_bib64","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-97219-5","article-title":"The influence of the marker set on inverse kinematics results to inform markerless motion capture annotations","volume":"15","author":"Mundt","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.gaitpost.2025.110083_bib65","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.jbiomech.2016.03.052","article-title":"Joint kinematic calculation based on clinical direct kinematic versus inverse kinematic gait models","volume":"49","author":"Kainz","year":"2016","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib66","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.jbiomech.2019.01.001","article-title":"Subject-specific and group-based running pattern classification using a single wearable sensor","volume":"84","author":"Ahamed","year":"2019","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib67","doi-asserted-by":"crossref","DOI":"10.3389\/fnbot.2022.913052","article-title":"Recent machine learning progress in lower limb running biomechanics with wearable technology: a systematic review","volume":"16","author":"Xiang","year":"2022","journal-title":"Front. Neurorob."},{"key":"10.1016\/j.gaitpost.2025.110083_bib68","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. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib69","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2245","article-title":"Trade-off between training and testing ratio in machine learning for medical image processing","volume":"10","author":"Sivakumar","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.gaitpost.2025.110083_bib70","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0224365","article-title":"Machine learning algorithm validation with a limited sample size","volume":"14","author":"Vabalas","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.gaitpost.2025.110083_bib71","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbiomech.2021.110820","article-title":"Comparing shallow, deep, and transfer learning in predicting joint moments in running","volume":"129","author":"Liew","year":"2021","journal-title":"J. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib72","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-70341-6","article-title":"A comparison between machine and deep learning models on high stationarity data","volume":"14","author":"Santoro","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.gaitpost.2025.110083_bib73","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s40537-023-00727-2","article-title":"A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications","volume":"10","author":"Alzubaidi","year":"2023","journal-title":"J. Big Data"},{"key":"10.1016\/j.gaitpost.2025.110083_bib74","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1177\/09544119251315877","article-title":"Synthetic data generation in motion analysis: A generative deep learning framework","volume":"239","author":"Perrone","year":"2025","journal-title":"Proc. Inst. Mech. Eng. H."},{"key":"10.1016\/j.gaitpost.2025.110083_bib75","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106188","article-title":"A fast calibration method for an sEMG-based lower limb joint torque estimation model","volume":"93","author":"Zhang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.gaitpost.2025.110083_bib76","first-page":"1","article-title":"Exploring dataset bias and scaling techniques in multi-source gait biomechanics: an explainable machine learning approach","volume":"20","author":"Fleischmann","year":"2025","journal-title":"ACM Trans. Intell. Syst. Technol. 16"},{"key":"10.1016\/j.gaitpost.2025.110083_bib77","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.clinbiomech.2008.08.003","article-title":"Surface electromyography and muscle force: Limits in sEMG\u2013force relationship and new approaches for applications","volume":"24","author":"Disselhorst-Klug","year":"2009","journal-title":"Clin. Biomech."},{"key":"10.1016\/j.gaitpost.2025.110083_bib78","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2024.1457623","article-title":"iP3T: an interpretable multimodal time-series model for enhanced gait phase prediction in wearable exoskeletons","volume":"18","author":"Chen","year":"2024","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.gaitpost.2025.110083_bib79","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0320793","article-title":"Can we use lower extremity joint moments predicted by the artificial intelligence model during walking in patients with cerebral palsy in the clinical gait analysis?","volume":"20","author":"Salami","year":"2025","journal-title":"PLoS One"},{"key":"10.1016\/j.gaitpost.2025.110083_bib80","first-page":"321","article-title":"Biomechanical analysis of human gait when changing velocity and carried loads: simulation study with OpenSim","volume":"13","author":"Brambilla","year":"2024","journal-title":"Biology (Basel)"},{"key":"10.1016\/j.gaitpost.2025.110083_bib81","doi-asserted-by":"crossref","first-page":"2939","DOI":"10.3390\/s20102939","article-title":"Estimating lower extremity running gait kinematics with a single accelerometer: a deep learning approach","volume":"20","author":"Gholami","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.gaitpost.2025.110083_bib82","doi-asserted-by":"crossref","DOI":"10.1142\/S0219876225500070","article-title":"A novel approach for prediction of peak plantar pressure during the gait cycle based on the LSTM and RNN","volume":"22","author":"Li","year":"2025","journal-title":"Int. J. Comput. Methods"},{"key":"10.1016\/j.gaitpost.2025.110083_bib83","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2024.1372669","article-title":"3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units","volume":"12","author":"Mohammadi Moghadam","year":"2024","journal-title":"Front. Bioeng. Biotechnol."}],"container-title":["Gait &amp; Posture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0966636225008100?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0966636225008100?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:45:17Z","timestamp":1778517917000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0966636225008100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":83,"alternative-id":["S0966636225008100"],"URL":"https:\/\/doi.org\/10.1016\/j.gaitpost.2025.110083","relation":{},"ISSN":["0966-6362"],"issn-type":[{"value":"0966-6362","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in lower limb joint moment prediction during typically developed gait: A systematic review and multilevel random-effects meta-analysis","name":"articletitle","label":"Article Title"},{"value":"Gait & Posture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.gaitpost.2025.110083","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"110083"}}