Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors.

ABSTRACT:

Objective

Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge.

Methods

Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outco

SUBMITTER: O'Brien MK 

PROVIDER: S-EPMC10851859 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature