AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale
Keenon Werling, Janelle Kaneda, Alan Tan, Rishi Agarwal, Six Skov, Tom, Van Wouwe, Scott Uhlrich, Nicholas Bianco, Carmichael Ong, Antoine Falisse,, Shardul Sapkota, Aidan Chandra, Joshua Carter, Ezio Preatoni, Benjamin, Fregly, Jennifer Hicks, Scott Delp, C. Karen Liu

TL;DR
The AddBiomechanics Dataset provides a large-scale, high-quality collection of human motion and force data to facilitate research in human dynamics and physics estimation from pose data.
Contribution
This paper introduces the first large-scale dataset with detailed human motion and force data, along with analytical methods and benchmarks for estimating human dynamics.
Findings
Over 70 hours of motion and force data collected from 273 subjects
Benchmark results for human dynamics estimation established
Novel analytical methods for dataset construction proposed
Abstract
While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of movements. We present the AddBiomechanics Dataset 1.0, which includes physically accurate human dynamics of 273 human subjects, over 70 hours of motion and force plate data, totaling more than 24 million frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results. The AddBiomechanics Dataset is publicly available at…
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Taxonomy
TopicsSports Dynamics and Biomechanics · Mechanics and Biomechanics Studies · Robotic Locomotion and Control
