Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations

TL;DR

This paper introduces Muscles in Time (MinT), a large-scale synthetic dataset of muscle activations generated from motion capture data, enabling improved neural network-based human muscle activation estimation.

Contribution

The creation of MinT, a comprehensive synthetic dataset with over nine hours of muscle activation simulations, addressing data scarcity in human motion research.

Findings

Neural networks can effectively estimate muscle activations from pose sequences.

The dataset enables new research in muscle activation modeling.

Open-source data and code facilitate further studies.

Abstract

Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attributed to the intensive resources required for acquiring ground truth muscle activation data, resulting in a scarcity of datasets. In this work, we address this issue by establishing Muscles in Time (MinT), a large-scale synthetic muscle activation dataset. For the creation of MinT, we enriched existing motion capture datasets by incorporating muscle activation simulations derived from biomechanical human body models using the OpenSim platform, a common approach in biomechanics and human motion research. Starting from simple pose sequences, our pipeline enables us to extract detailed information about the timing of muscle activations within the human musculoskeletal system. Muscles in Time contains over nine hours of simulation data covering 227 subjects and 402 simulated muscle strands. We demonstrate the utility of this dataset by presenting results on neural network-based muscle activation estimation from human pose sequences with two different sequence-to-sequence architectures. Data and code are provided under https://simplexsigil.github.io/mint.