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1/8 Our paper “Movement science needs different pose tracking algorithms” is on arxiv. #tweeprint
arxiv.org/abs/1907.10226
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arxiv.org/abs/1907.10226
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2/8 In this paper, we give ideas for how pose estimation algorithms should change to best serve movement science -- by quantifying different variables, better ground truth, tracking in time, and more...
3/8 Many fields of science and engineering rely on movement data for research. Insights from movement data impact neuroscience, bioengineering, sports science, psychology, physiology, biophysics, robotics and even more fields 
4/8 Exciting progress in pose estimation in-the-wild promises to take movement science outside the lab: study real-world non-contrived movements, increase number of subjects, realize low-cost science and medicine & do science on existing videos i.e. not run experiments ourselves.
5/8 But, existing algorithms don’t serve the needs of movement science yet. Main reason for this is: they largely ignore underlying dynamics and treat each video frame as independent from its neighbors. In reality, each frame imposes a strong prior for poses in nearby frames.
6/8 Secondly, because they ignore the physical range of motion constraints imposed by our body. These oversights (among others) result is weird errors when tracking videos of interest to movement science:
7/8 A summary of our main suggestions on how to change pose tracking to best serve movement science is provided in the table below:
