Gait Reconstruction from Motion Artefact Corrupted Fabric-Embedded Sensors

TY - JOUR

T1 - Gait Reconstruction from Motion Artefact Corrupted Fabric-Embedded Sensors

AU - Michael, Brendan

AU - Howard, Matthew Jacob William

PY - 2018/7

Y1 - 2018/7

N2 - "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 paper, 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 paper 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.

AB - "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 paper, 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 paper 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.

KW - Wearable sensors

KW - Adaptive learning

KW - Machine learning

KW - Fabric sensors

U2 - 10.1109/LRA.2018.2807810

DO - 10.1109/LRA.2018.2807810

M3 - Letter

VL - 3

SP - 1918

EP - 1924

JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

IS - 3

ER -