Gait Reconstruction from Motion Artefact Corrupted Fabric-Embedded Sensors

This research investigates the use of unsupervised latent space learning techniques for the removal of motion artefacts in fabric embedded sensor systems. This dataset contains motion data collected during walking tasks, for two sensor systems,
1) a high-quality ground truth inertial measurement system, and 2) tri-axel linear accelleration and angular velocity measurements from sensor embedded into items of clothing.