Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data.

ABSTRACT: Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patien

SUBMITTER: Ogasawara T 

PROVIDER: S-EPMC10791943 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature