An AI-based Fall Risk Assessment Tool for Stroke Survivors: Implementing Detailed Motion Analysis Features while Performing Motor-Cognitive Dual-Tasks

August 4, 2023 Ehsan Rashedi Faculty: Ehsan Rashedi

Harnessing the advantages of motion sensors, along with the capabilities of machine learning models, this study aims to solve the long-lasting problem of falls in stroke survivors. The objective of this study is to develop a data-driven fall risk assessment model that utilizes motion data collected from the body segments of stroke survivors while they perform common activities of daily living and motor-cognitive dual tasks.