Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing

Simmatis, Leif; Early, Spencer; Moore, Kimberly D.; Appaqaq, Simone; Scott, Stephen H.

doi:10.1186/s12984-020-00713-2

Cited by 29 publications

(44 citation statements)

References 55 publications

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“…The reliability results obtained for the motor and sensorimotor task are in line with the ones previously reported in literature (ICC 0.7-0.9 for metrics of good quality) [ 12 , 74 – 76 ], although it is challenging to directly compare these, as some studies were performed with control subjects instead of the target population. Compared to conventional clinical assessments, the proposed robotic assessments generally result in higher measurement error (e.g.…”

Section: Discussionsupporting

confidence: 88%

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke

Zbytniewska

Kanzler

Jordan

et al. 2021

J NeuroEngineering Rehabil

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Background Neurological injuries such as stroke often differentially impair hand motor and somatosensory function, as well as the interplay between the two, which leads to limitations in performing activities of daily living. However, it is challenging to identify which specific aspects of sensorimotor function are impaired based on conventional clinical assessments that are often insensitive and subjective. In this work we propose and validate a set of robot-assisted assessments aiming at disentangling hand proprioceptive from motor impairments, and capturing their interrelation (sensorimotor impairments). Methods A battery of five complementary assessment tasks was implemented on a one degree-of-freedom end-effector robotic platform acting on the index finger metacarpophalangeal joint. Specifically, proprioceptive impairments were assessed using a position matching paradigm. Fast target reaching, range of motion and maximum fingertip force tasks characterized motor function deficits. Finally, sensorimotor impairments were assessed using a dexterous trajectory following task. Clinical feasibility (duration), reliability (intra-class correlation coefficient ICC, smallest real difference SRD) and validity (Kruskal-Wallis test, Spearman correlations $$\rho$$ ρ with Fugl-Meyer Upper Limb Motor Assessment, kinesthetic Up-Down Test, Box & Block Test) of robotic tasks were evaluated with 36 sub-acute stroke subjects and 31 age-matched neurologically intact controls. Results Eighty-three percent of stroke survivors with varied impairment severity (mild to severe) could complete all robotic tasks (duration: <15 min per tested hand). Further, the study demonstrated good to excellent reliability of the robotic tasks in the stroke population (ICC>0.7, SRD<30%), as well as discriminant validity, as indicated by significant differences (p-value<0.001) between stroke and control subjects. Concurrent validity was shown through moderate to strong correlations ($$\rho$$ ρ =0.4-0.8) between robotic outcome measures and clinical scales. Finally, robotic tasks targeting different deficits (motor, sensory) were not strongly correlated with each other ($$\rho \le$$ ρ ≤ 0.32, p-value>0.1), thereby presenting complementary information about a patient’s impairment profile. Conclusions The proposed robot-assisted assessments provide a clinically feasible, reliable, and valid approach to distinctly characterize impairments in hand proprioceptive and motor function, along with the interaction between the two. This opens new avenues to help unravel the contributions of unique aspects of sensorimotor function in post-stroke recovery, as well as to contribute to future developments towards personalized, assessment-driven therapies.

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Section: Discussionsupporting

confidence: 88%

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke

Zbytniewska

Kanzler

Jordan

et al. 2021

J NeuroEngineering Rehabil

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“…Demonstrating the reliability of the proprioception assessment (gauge position matching task) is an important contribution, since it is a long-standing challenge to reliably evaluate proprioceptive hand impairments in neurological patients, using either conventional or robotic methods [23,35]. In addition, the reliability results obtained for the motor and sensorimotor task are in line with the ones previously reported in literature (ICC 0.7-0.9 for metrics of good quality) [11,[71][72][73], although it is challenging to directly compare these, as some studies were performed with control subjects instead of the target population. Compared to conventional clinical assessments, the proposed robotic assessments generally result in higher measurement error (e.g.…”

Section: Test-retest Reliabilitysupporting

confidence: 80%

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke

Zbytniewska

Kanzler

Jordan

et al. 2020

Preprint

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Background: Neurological injuries such as stroke often differentially impair hand motor and somatosensory function, as well as the interplay between the two, which leads to limitations in performing activities of daily living. However, it is challenging to identify which specific aspects of sensorimotor function are impaired based on conventional clinical assessments that are often insensitive and subjective. In this work we propose and validate a set of robot-assisted assessments aiming at disentangling hand proprioceptive from motor impairments, and capturing their interrelation (sensorimotor impairments). Methods: For this purpose, a battery of five complementary assessment tasks was implemented on a one degree-of-freedom end-effector robotic platform acting on the index finger metacarpophalangeal joint. Specifically, proprioceptive impairments were assessed using a position matching paradigm. Fast target reaching, range of motion and maximum fingertip force tasks characterized motor function deficits. Finally, sensorimotor impairments were assessed using a dexterous trajectory following task. Clinical feasibility (duration), reliability (intra-class correlation coefficient ICC, smallest real difference SRD) and validity (Kruskal-Wallis test, Spearman correlations rho) of these tasks were evaluated with 36 sub-acute stroke subjects and 31 age-matched neurologically intact controls. Results: Eighty-three percent of stroke survivors with varied impairment severity (mild to severe) could complete all robotic tasks (duration: <15 min per tested hand). Further, the study demonstrated good to excellent reliability of the robotic tasks in the stroke population (ICC>0.7, SRD<30%), as well as discriminant validity, as indicated by significant differences (p-value<0.001) between stroke and control subjects on all metrics extracted from the robotic tasks. Concurrent validity was shown through moderate to strong correlations (rho=0.4-0.8) between the robotic outcome measures and clinical scales. Finally, robotic tasks targeting different deficits were not strongly correlated, thereby presenting complementary information about a patient’s impairment profile. Conclusions: The proposed robot-assisted assessments provide a clinically feasible, reliable, and valid approach to distinctly characterize impairments in hand proprioceptive and motor function, along with the interaction between the two. This opens new avenues to help unravel the contributions of unique aspects of sensorimotor function in post-stroke recovery, as well as to contribute to future developments towards personalized, assessment-driven therapies.

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“…These equations were implemented in the software used to run the Kinarm tasks (Dexterit-E version 3.7; see text footnote 1) and generated Z -scores for measures of performance for each healthy individual. This process has been described in greater detail previously ( BKIN Technologies, 2019 ; Simmatis et al, 2020a ).…”

Section: Methodsmentioning

confidence: 99%

“…Principal component analysis was conducted in R version 3.4.1 ( R Core Team, 2017 ) using psych package version 1.8.12 ( Revelle, 2019 ). Kinarm performance items were converted to Z-scores ( Simmatis et al, 2020a ) prior to PCA, with parallel analysis being used to determine the optimal number of components. Eigenvalues (i.e., proxies of relative variance explained by each component) were plotted in descending order to generate a scree plot ( Cattell, 1966 ; Sakaluk and Short, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Principal Components Analysis Using Data Collected From Healthy Individuals on Two Robotic Assessment Platforms Yields Similar Behavioral Patterns

Wood

Simmatis

Jacobson

et al. 2021

Front. Hum. Neurosci.

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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 (N = 469) and EP (N = 170–200). Four behavioral tasks (six assessments in total) were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching (APM)] and three motor tasks [Visually Guided Reaching (VGR), Object Hit (OH), and Object Hit and Avoid (OHA)]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type (orthogonal vs. oblique) was decided on a per-task basis. To assess agreement, we compared principal components (PCs) 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 (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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Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing

Cited by 29 publications

References 55 publications

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke

Principal Components Analysis Using Data Collected From Healthy Individuals on Two Robotic Assessment Platforms Yields Similar Behavioral Patterns

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