1 research outputs found
Movement science needs different pose tracking algorithms
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.Comment: 13 pages, 2 figures, 1 tabl