Movement science needs different pose tracking algorithms
This work addresses the gap between computer vision pose tracking and the specific data requirements of movement science, which is incremental as it critiques existing methods without introducing new algorithms.
The paper identifies that current pose tracking algorithms fail to meet the needs of movement science by poorly estimating crucial variables like 3D position and velocity, and proposes changes to make these algorithms more effective for real-world applications such as disease detection.
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.