ArticleFrontiers in human neuroscience2021
Principal Components Analysis Using Data Collected From Healthy Individuals on Two Robotic Assessment Platforms Yields Similar Behavioral Patterns.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Measure of Motion Similarity for Robotics Application.Sensors (Basel, Switzerland) · 2023Article
- The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke.Journal of neuroengineering and rehabilitation · 2023Article
- Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population.Scientific reports · 2022Article
- High intra-task and low inter-task correlations of motor skills in humans creates an individualized behavioural pattern.Scientific reports · 2022Article
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Authors and funding
6 authors.
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Abstract
backgroundKinarm Standard Tests (KSTs) is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior (>100 variables per individual). We have previously used principal component analysis (PCA) to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab (EP). Here, we performed PCA using data from the Kinarm Exoskeleton Lab (EXO), and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further dimensionality reduction was possible by using PCA across behavioral tasks.
methodsHealthy participants were assessed using the Kinarm EXO (
resultsBy applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58-75% while accounting for 76-87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values (0.75-0.99) between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.
conclusionPCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations.
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