Using principal component analysis to reduce complex datasets produced by robotic technology in healthy participants.

ABSTRACT:

Background

The KINARM robot produces a granular dataset of participant performance metrics associated with proprioceptive, motor, visuospatial, and executive function. This comprehensive battery includes several behavioral tasks that each generate 9 to 20 metrics of performance. Therefore, the entire battery of tasks generates well over 100 metrics per participant, which can make clinical interpretation challenging. Therefore, we sought to reduce these multivariate data by applying principal component analysis (PCA) to increase interpretability while minimizing information loss.

Methods

Healthy right-hand dominant participants were assessed using a bilateral KINARM end-point robot. Subjects (Ns = 101-208) were assessed using 6 behavioral tasks and automated software generate

SUBMITTER: Wood MD 

PROVIDER: S-EPMC6069537 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature