Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion.
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ABSTRACT:
SUBMITTER: Geissinger JH
PROVIDER: S-EPMC7664234 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion.
Geissinger Jack H JH Asbeck Alan T AT
Sensors (Basel, Switzerland) 20201106 21
In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics estimates using a small number of sensors. To accomplish this, we introduced a new dataset (the Virginia Tech Natural Motion Dataset) of full-body human motion capture using XSens MVN Link that contains more than 40 h of unscripted daily life motion i ...[more]
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