GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors

TL;DR

GAITEX is a comprehensive multimodal dataset combining inertial and optical sensor data for human gait and rehabilitation exercises, enabling improved machine learning models for clinical assessment and movement analysis.

Contribution

The paper introduces GAITEX, a large, annotated dataset with synchronized IMU and MoCap data, including processed biomechanical models and tools for diverse movement analysis tasks.

Findings

Dataset includes data from 19 subjects performing various exercises.

Provides synchronized IMU and optical motion capture data for comparison.

Supports multiple machine learning applications in gait and rehabilitation analysis.

Abstract

Wearable inertial measurement units (IMUs) provide a cost-effective approach to assessing human movement in clinical and everyday environments. However, developing the associated classification models for robust assessment of physiotherapeutic exercise and gait analysis requires large, diverse datasets that are costly and time-consuming to collect. We present a multimodal dataset of physiotherapeutic and gait-related exercises, including correct and clinically relevant variants, recorded from 19 healthy subjects using synchronized IMUs and optical marker-based motion capture (MoCap). It contains data from nine IMUs and 68 markers tracking full-body kinematics. Four markers per IMU allow direct comparison between IMU- and MoCap-derived orientations. We additionally provide processed IMU orientations aligned to common segment coordinate systems, subject-specific OpenSim models, inverse kinematics outputs, and visualization tools for IMU-derived orientations. The dataset is fully annotated with movement quality ratings and timestamped segmentations. It supports various machine learning tasks such as exercise evaluation, gait classification, temporal segmentation, and biomechanical parameter estimation. Code for postprocessing, alignment, inverse kinematics, and technical validation is provided to promote reproducibility.