Whereas the λ model provides a useful technique to describe complex movements, the focus on control variables in this model limits its potential for interpreting the activity and function of many cells in motor areas of the CNS.

BackgroundKinarm Standard Tests 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. We have previously used principal component analysis to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab. Here, we performed PCA using data from the Kinarm Exoskeleton Lab, 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 and EP. Four behavioral tasks were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching ] and three motor tasks [Visually Guided Reaching, Object Hit, and Object Hit and Avoid ]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type was decided on a per-task basis. To assess agreement, we compared principal components across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.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 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. (shrink)