A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors

Abstract

Abstract 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.