Dataset Information

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 generated 9 to 20 metrics related to the spatial and temporal aspects of subject performance. Data from these metrics were converted to Z-scores prior to PCA. The number of components was determined from scree plots and parallel analysis, with interpretability considered as a qualitative criterion. Rotation type (orthogonal vs oblique) was decided on a per task basis.

Results

The KINARM performance data, per task, was substantially reduced (range 67-79%), while still accounting for a large amount of variance (range 70-82%). The number of KINARM parameters reduced to 3 components for 5 out of 6 tasks and to 5 components for the sixth task. Many components were comprised of KINARM parameters with high loadings and only some cross loadings were observed, which demonstrates a strong separation of components.

Conclusions

Complex participant data produced by the KINARM robot can be reduced into a small number of interpretable components by using PCA. Future applications of PCA may offer potential insight into specific patterns of sensorimotor impairment among patient populations.

SUBMITTER: Wood MD

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

REPOSITORIES: biostudies-literature

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Publications

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

Wood Michael D MD Simmatis Leif E R LER Gordon Boyd J J Scott Stephen H SH Jacobson Jill A JA

Journal of neuroengineering and rehabilitation 20180731 1

<h4>Background</h4>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 princ ...[more]

PMID: 30064448

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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