Gait Reconstruction From Motion Artefact Corrupted Fabric-Embedded Sensors

Abstract

``Fabric-embedded sensors” are of growing interest in clinical diagnostics and rehabilitation studies that desire the measurement and analysis of human movement outside the laboratory environment. A major issue limiting their usage is the undesired effect of fabric motion artefacts corrupting movement signals. While supervised calibration methods can be used to eliminate these artefacts, these methods make assumptions on the fabric motion, and are unable to address changes in user motion (e.g., locomotion speed) or clothing deformation. In this letter, an unsupervised latent space regression method is presented for learning body movements from fabric motion corrupted sensors, while simultaneously allowing for automatic recalibration. Experiments in this letter show that unsupervised gait learning performs equally as well as supervised learning when removing motion artefacts. This allows for the implementation of adaptive motion artefact methods in real-world sensor-embedded clothing.