Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Background: Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. Objective: Develop machine learning models using inertial sensors to objectively classify fall risk in st...

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Bibliographic Details
Main Authors: Masoud Abdollahi, Ehsan Rashedi, Sonia Jahangiri, Pranav Madhav Kuber, Nasibeh Azadeh-Fard, Mary Dombovy
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Sensors
Subjects:
neurological disorder
wearable sensors
motion analysis
fall
neuroscience
TUG
Online Access:https://www.mdpi.com/1424-8220/24/3/812

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https://www.mdpi.com/1424-8220/24/3/812

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