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

Abstract

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.

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

“…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).…”

Achieving Precision Healthcare through Nanomedicine and Enhanced Model Systems

“…Several studies have addressed joint behavior under various conditions including loading, lubrication, and mechanics of the cartilage and surrounding tissues. 61,62 However, they rely on accurate input data and assumptions, which may introduce uncertainties and simplifications. 63,64 For example, while there are numerous studies addressing inorganic or carbon-based NC transport, polymeric NC models are more difficult to build due to more complex structural properties and more sensitive physicochemical parameters.…”

“…Based on mathematical equations and computer-driven analysis, models such as finite element analysis (FEA) or multibody dynamic simulations can provide valuable insights into tissue mechanics, function, and NC toxicity. , They offer a cost-effective approach to study joint biomechanics and allow rapid exploration of different scenarios and parameter variations. Several studies have addressed joint behavior under various conditions including loading, lubrication, and mechanics of the cartilage and surrounding tissues. , However, they rely on accurate input data and assumptions, which may introduce uncertainties and simplifications. , For example, while there are numerous studies addressing inorganic or carbon-based NC transport, polymeric NC models are more difficult to build due to more complex structural properties and more sensitive physicochemical parameters . The complexity of the NC-cell interactions also poses an additional challenge where a vast majority of descriptors must be included to replicate the dynamic nature of cellular membranes and the glycocalyx. , Such drawbacks may make computational models generalized or biased, missing out on biologically relevant complexities of in vivo joint physiology.…”