A combination of wearable sensors’ data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints’ kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys’ (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors’ data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets’ prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors’ data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.

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Gait analysis outside the laboratory has been possible by recent advancements in wearable sensors like inertial measurement units (IMUs) and Electromypgraphy (EMG) sensors. The aim of this study was to compare performance of four different non-linear regression machine learning (ML) models to estimate lower-limb joints’ kinematics, kinetics, and muscle forces using IMUs and EMGs’ data. Seventeen healthy volunteers (9F, 28 ± 5 yrs) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets) as well as 7 IMUs and 16 EMGs. The most important features from sensors’ data were extracted using Tsfresh python package and fed into 4 ML models; Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) for targets’ prediction. The RF model outperformed the other ML models by providing lower prediction errors in all intended targets. This study suggested that a combination of wearable sensors’ data with an RF model is a promising tool to overcome limitations of traditional optical motion capture for 3D gait analysis.