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

Author:

Moghadam Shima Mohammadi1,Auriol Pablo Ortega1,Yeung Ted1,Choisne Julie1

Affiliation:

1. The University of Auckland

Abstract

Abstract Children's walking patterns evolve with age, exhibiting less repetitiveness at a young age and more variability than adults. Three-dimensional gait analysis (3DGA) is crucial for understanding and treating lower limb movement disorders in children, traditionally performed using Optical Motion Capture (OMC). Inertial Measurement Units (IMUs) offer a cost-effective alternative to OMC, although challenges like drift errors persist. Machine learning (ML) models can mitigate these issues in adults, prompting an investigation into their applicability to a heterogeneous paediatric population. This study aimed at identifying an optimal ML model tailored for children's gait, enabling accurate predictions from IMUs. Seventeen typically developed (TD) children, aged 6 to 15, participated in data collection involving OMC, force plates, and IMU sensors. Joint kinematics and kinetics were computed from OMC and force plates' data using OpenSim. Tsfresh, a Python package, extracted features from raw IMU data. Each target's ten most important features were input in the development of personalized and generalized random forest (RF) and convolutional neural networks (CNN) models. Findings suggested that the RF and CNN models demonstrated comparable performance. RF predicted joint kinematics with a 9.5% and 19.9% NRMSE for personalized and generalized models, respectively, and joint kinetics with an NRMSE of 10.7% for personalized and 15.2% for generalized models in TD children. Personalized models provided accurate estimations from IMU data in children, while generalized models lacked accuracy due to the limited dataset. This study proposed a promising approach for gait time series prediction in children, involving an RF model and two IMUs on the feet.

Publisher

Research Square Platform LLC

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