Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network.

ABSTRACT:

Background

Gait analysis using three-dimensional motion capture systems (3D motion capture) provides a combination of kinematic and kinetic measurements for quantifying and characterizing the motion and loads, respectively, of lower extremity joints during human movement. However, their high cost and limited accessibility impact their utility. Wearable inertial motion sensors offer a cost-effective alternative to measure simple temporospatial variables, but more complex kinematic variables require machine learning interfaces. We hypothesize that kinematic measures about the knee collected using motion capture can be replicated by coupling raw data collected from inertial measurement units (IMUs) to machine learning algorithms.

Methods

Data from 40 healthy participants perfor

SUBMITTER: Bini SA 

PROVIDER: S-EPMC12020888 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature