AUTHOR=Xiang Liangliang , Wang Alan , Gu Yaodong , Zhao Liang , Shim Vickie , Fernandez Justin TITLE=Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review JOURNAL=Frontiers in Neurorobotics VOLUME=Volume 16 - 2022 YEAR=2022 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.913052 DOI=10.3389/fnbot.2022.913052 ISSN=1662-5218 ABSTRACT=With 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 4068 articles were identified via electronic databases. Twenty-four articles met the eligibility criteria after article screening was included in this systematic review. The range of quality scores of the included studies are from 0.78 to 1.00, with 40% of articles recruiting participant numbers between 20 to 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, test or validation of the machine learning models were lacking and needed for future studies. 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.