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

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.

3D Gait Analysis in Children Using Wearable Sensors: Feasibility of Predicting Joint Kinematics and Kinetics with Personalized Machine Learning Models and Inertial Measurement Units

“…The RMSE values for joint kinematics stayed well below the 5° error threshold, which is often considered a clinically acceptable level of deviation for assessing joint movements(Slater et al, 2018). However, the joint kinematics exhibited a slight increase compared to personalized models developed for adults in other studies, where observed values ranged from 1.38° to 3.96° for all targets(Moghadam et al, 2023a, Yeung et al, 2023, Findlow et al, 2008, Giarmatzis et al, 2020.…”

“…2, Step 5). The hyperparameters for both RF and CNN models were chosen based on previously optimized models (Moghadam et al, 2023a). We employed an RF model comprising 500 trees, each with a maximum depth of 25.…”

“…However, there is a limited body of literature exploring alternative data-driven models that may demand smaller datasets while achieving comparable results to ANNs. Building on this context, in a prior study, we demonstrated that Random Forest (RF) models can yield results comparable to more intricate machine learning models such as Convolutional Neural Networks (CNNs) for 3D Gait Analysis (3DGA) in adults (Moghadam et al, 2023a). Given the greater heterogeneity in children's gait, it will be interesting to explore whether RF or CNNs can be applied to a paediatric population with similar performances.…”

“…The challenges associated with processing IMU data in adult populations have been addressed in previous studies (Moghadam et by implementing Machine learning (ML) models. These ML models can establish a direct relationship between the IMUs' data and OMC derived gait time series (joint kinematics, joint kinetics, and muscle forces) (Moghadam et al, 2023a). Prior research indicated the e cacy of this approach in adult populations, demonstrating highly accurate results with low errors during personalized model (tested on the same individual used for training).…”

Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model

“…Endzhievskaya et al [31] suggested a preference for using the RF model in machine learning due to its ease of use and minimum number of hyperparameters for tuning. Compared to machine learning models such as DT, CNN, and LSTM, the RF model has demonstrated higher predictive accuracy in certain application scenarios [32][33][34]. The aforementioned studies indicate that the RF model may offer better predictive performance in data prediction.…”