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Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion

1
Department of Electrical & Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA
2
Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6330; https://doi.org/10.3390/s20216330
Received: 13 August 2020 / Revised: 27 October 2020 / Accepted: 30 October 2020 / Published: 6 November 2020
(This article belongs to the Section Wearables)
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 in the open world. Using this dataset, we conducted self-supervised machine learning to do kinematics inference: we predicted the complete kinematics of the upper body or full body using a reduced set of sensors (3 or 4 for the upper body, 5 or 6 for the full body). We used several sequence-to-sequence (Seq2Seq) and Transformer models for motion inference. We compared the results using four different machine learning models and four different configurations of sensor placements. Our models produced mean angular errors of 10–15 degrees for both the upper body and full body, as well as worst-case errors of less than 30 degrees. The dataset and our machine learning code are freely available. View Full-Text
Keywords: motion dataset; kinematics; inertial sensors; self-supervised learning; sparse sensors motion dataset; kinematics; inertial sensors; self-supervised learning; sparse sensors
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MDPI and ACS Style

Geissinger, J.H.; Asbeck, A.T. Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion. Sensors 2020, 20, 6330. https://doi.org/10.3390/s20216330

AMA Style

Geissinger JH, Asbeck AT. Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion. Sensors. 2020; 20(21):6330. https://doi.org/10.3390/s20216330

Chicago/Turabian Style

Geissinger, Jack H., and Alan T. Asbeck. 2020. "Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion" Sensors 20, no. 21: 6330. https://doi.org/10.3390/s20216330

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