Xiang, LiangliangWang, AlanGu, YaodongZhao, LiangShim, VickieFernandez, Justin2023-08-062023-08-062022-01(2022). Frontiers in Neurorobotics, 16, 913052-.1662-5218https://hdl.handle.net/2292/65407With the emergence of wearable technology and machine learning approaches, gait monitoring in real-time is attracting interest from the sports biomechanics community. This study presents a systematic review of machine learning approaches in running biomechanics using wearable sensors. Electronic databases were retrieved in PubMed, Web of Science, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect. A total of 4,068 articles were identified <i>via</i> electronic databases. Twenty-four articles that met the eligibility criteria after article screening were included in this systematic review. The range of quality scores of the included studies is from 0.78 to 1.00, with 40% of articles recruiting participant numbers between 20 and 50. The number of inertial measurement unit (IMU) placed on the lower limbs varied from 1 to 5, mainly in the pelvis, thigh, distal tibia, and foot. Deep learning algorithms occupied 57% of total machine learning approaches. Convolutional neural networks (CNN) were the most frequently used deep learning algorithm. However, the validation process for machine learning models was lacking in some studies and should be given more attention in future research. The deep learning model combining multiple CNN and recurrent neural networks (RNN) was observed to extract different running features from the wearable sensors and presents a growing trend in running biomechanics.Electronic-eCollectionItems in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htmhttps://creativecommons.org/licenses/by/4.0/deep learninggaitlower limbmachine learningrunningwearable sensorBioengineering0801 Artificial Intelligence and Image Processing0903 Biomedical Engineering1109 NeurosciencesJournal Article10.3389/fnbot.2022.9130522023-07-11Copyright: The authors35721274 (pubmed)http://purl.org/eprint/accessRights/OpenAccess1662-5218