Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks.
Benjamin Filtjens, Pieter Ginis, Alice Nieuwboer and 2 others
PMID 35597950WHAT IT FOUND
A motion-capture model detected freezing episodes in seven people with Parkinson's disease.
It matched expert ratings closely for percent time frozen and less closely for episode count, but was tested only in a lab protocol.
Key findings
01The model detected 52 of 56 freezing episodes labelled by experts and produced six false-positive freezing episodes.
02The model's percent-time-frozen estimates had a very strong linear relationship with expert ratings (r = 0.93), while its freezing-episode counts had a moderately strong relationship (r = 0.75).
03No false-positive freezing segments were predicted in trials from healthy controls, non-freezers, or freezers who did not freeze during the protocol.
STILL TO COME
How it was doneWhat they foundWhat it means for PTs
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What it does not show
Only seven Parkinson's participants who froze during the lab protocol were used to test the model, so the evaluation is small. The test used a standardized walkway with straight walking and turns, not everyday walking or other freezing triggers. Freezing can be hard to provoke in a lab or clinic, and lab severity may not match daily-life severity. The model overestimated severity for one participant, underestimated it for another, and missed two freezing episodes shorter than half a second. Dataset 2 was used to enrich training but not to test the model. All gait assessments were done after an overnight medication withdrawal.
The easy way to misread this
Do not treat this as a clinical test for freezing of gait. It was evaluated only on seven people who froze during a standardized motion-capture lab protocol, and it still overestimated or underestimated freezing severity for some participants.