# Movement science needs different pose tracking algorithms

arXiv: https://arxiv.org/abs/1907.10226 · HTML: https://mlsanity.com/paper/1907.10226/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 10 of 10)

- [Markerless human pose estimation for biomedical applications: a survey](https://mlsanity.com/paper/2308.00519/) (2023)
- [PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research](https://mlsanity.com/paper/2203.08792/) (2022)
- [Transforming Gait: Video-Based Spatiotemporal Gait Analysis](https://mlsanity.com/paper/2203.09371/) (2022)
- [Towards Single Camera Human 3D-Kinematics](https://mlsanity.com/paper/2301.05435/) (2023)
- [Automatic infant 2D pose estimation from videos: comparing seven deep neural network methods](https://mlsanity.com/paper/2406.17382/) (2024)
- [Image-based Stability Quantification](https://mlsanity.com/paper/2206.11443/) (2022)
- [Learning to Estimate Critical Gait Parameters from Single-View RGB Videos with Transformer-Based Attention Network](https://mlsanity.com/paper/2312.00398/) (2023)
- [Grounding Intelligence in Movement](https://mlsanity.com/paper/2507.02771/) (2025)
- [Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction](https://mlsanity.com/paper/2208.06494/) (2022)
- [Nonlinear Methods for Analyzing Pose in Behavioral Research](https://mlsanity.com/paper/2604.01453/) (2026)

## Abstract (arXiv)

Over the last decade, computer science has made progress towards extracting body pose from single camera photographs or videos. This promises to enable movement science to detect disease, quantify movement performance, and take the science out of the lab into the real world. However, current pose tracking algorithms fall short of the needs of movement science; the types of movement data that matter are poorly estimated. For instance, the metrics currently used for evaluating pose tracking algorithms use noisy hand-labeled ground truth data and do not prioritize precision of relevant variables like three-dimensional position, velocity, acceleration, and forces which are crucial for movement science. Here, we introduce the scientific disciplines that use movement data, the types of data they need, and discuss the changes needed to make pose tracking truly transformative for movement science.