Real-Time Prediction of Lower Limb Joint Kinematics, Kinetics, and Ground Reaction Force using Wearable Sensors and Machine Learning

TL;DR

This study introduces a real-time, wearable sensor-based framework using machine learning to accurately predict lower limb joint kinematics, kinetics, and ground reaction forces during walking, enabling immediate biofeedback applications.

Contribution

It presents a novel multimodal, high-rate motion capture system that fully relies on wearable sensors and machine learning, covering all major lower limb joints with minimal delay.

Findings

Achieved accurate joint angle predictions from IMU data.

Predicted ground reaction force with high accuracy from insoles.

Provided joint moments estimation with minimal delay of 23 ms.

Abstract

Walking is a key movement of interest in biomechanics, yet gold-standard data collection methods are time- and cost-expensive. This paper presents a real-time, multimodal, high sample rate lower-limb motion capture framework, based on wireless wearable sensors and machine learning algorithms. Random Forests are used to estimate joint angles from IMU data, and ground reaction force (GRF) is predicted from instrumented insoles, while joint moments are predicted from angles and GRF using deep learning based on the ResNet-16 architecture. All three models achieve good accuracy compared to literature, and the predictions are logged at 1 kHz with a minimal delay of 23 ms for 20s worth of input data. The present work fully relies on wearable sensors, covers all five major lower limb joints, and provides multimodal comprehensive estimations of GRF, joint angles, and moments with minimal delay suitable for biofeedback applications.