Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors

Overview

Journal IEEE Trans Biomed Eng

Specialties Biomedical Engineering
Biophysics

Date 2020 Sep 30

PMID 32997621

Citations 10

Authors

Sunghoon I Lee

Catherine P Adans-Dester

Anne T OBrien

Gloria P Vergara-Diaz

Randie Black-Schaffer

Ross Zafonte

Jennifer G Dy

Paolo Bonato

Affiliations

Soon will be listed here.

Abstract

Objective: Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients' response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis.

Methods: Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two.

Results: The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson's correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimates with a Pearson's correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson's correlation of 0.94 between estimated and clinician-provided scores.

Conclusion: This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data.

Significance: This is important in the context of developing precision rehabilitation interventions.

Citing Articles

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AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.

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Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors' Motor Ability.

Lee S, Liu Y, Vergara-Diaz G, Pugliese B, Black-Schaffer R, Stoykov M Neurorehabil Neural Repair. 2024; 38(9):659-669.

PMID: 39109662 PMC: 11405131. DOI: 10.1177/15459683241270066.

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