AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale

• URL: http://arxiv.org/abs/2406.18537v1
• Date: Thu, 16 May 2024 16:57:43 GMT
• Title: AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale
• Authors: 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,
• Abstract summary: We present the AddBiomechanics dataset 1.0, which includes physically accurate human dynamics of 273 human subjects. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results.
• Score: 6.704034895950097
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://addbiomechanics.org/download_data.html.

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