Using principal component analysis to reduce complex datasets produced by robotic technology in healthy participants.
Michael D Wood, Leif E R Simmatis, J Gordon Boyd and 2 others
PMID 30064448WHAT IT FOUND
Robotic upper-limb test scores can be grouped into a few readable patterns.
In healthy right-handed adults, each task’s many metrics reduced to 3 or 5 patterns explaining most of the differences.
Key findings
01For Arm Position Matching, Ball on Bar, Visually Guided Reaching, Reverse Visually Guided Reaching, and Object Hit, 9 to 14 metrics were reduced to 3 components, accounting for 70–79% of variance.
02Object Hit and Avoid was reduced from 20 performance items to 5 components, while accounting for 82% of variance.
03Only 11 participants completed all 6 tasks, and the analysis included right-hand dominant participants only.
STILL TO COME
How it was doneWhat they foundWhat it means for PTs
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What it does not show
The study used healthy people only, so it does not show what these components mean in stroke, brain injury, or other patient groups. It included only right-handed people, so left-handed performance may differ. Each task was analyzed separately because not everyone completed all tasks; only 11 participants completed all 6 tasks. Some metrics were excluded because they could not be converted to standardized scores. The analysis was done only on the End-Point robot, not the KINARM Exoskeleton. Some metrics loaded onto more than one component, and a few did not load onto any component. The study did not examine individual or group differences within the healthy sample.
Declared interests
The supplied text does not include a conflicts-of-interest or funding declaration.
The easy way to misread this
Do not use these components as patient norms or diagnostic thresholds. They were derived from healthy right-handed people using the End-Point robot, and the paper says the components still need validation in clinical populations.