mlsanityData to 14 Oct 2026

Measuring and modeling the motor system with machine learning

arXiv 2103.11775 ↗First posted 22 Mar 2021q-bio.QM cs.CV cs.LGcited by 7

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Abstract (arXiv)

The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 26 of its 150 references that are in the map. First posted on arXiv 22 Mar 2021.

Builds on

26 of 150 references.

References · 26

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14 more
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1; 2025: 1.

7 papers.

  1. 2023Brain decoding: toward real-time reconstruction of visual perceptioncited by 66
  2. 2022SuperAnimal pretrained pose estimation models for behavioral analysiscited by 39
  3. 2021AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wildcited by 30
  4. 2023Latent Exploration for Reinforcement Learningcited by 22
  5. 2022A Rubric for Human-like Agents and NeuroAIcited by 8
  6. 2024Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systemscited by 5
  7. 2025Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movementscited by 1