Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data

Launched by SEOUL NATIONAL UNIVERSITY HOSPITAL · Apr 17, 2024

Keywords

ClinConnect Summary

This clinical trial is studying a new way to predict the risk of falls in patients who have had a stroke. Researchers are using a machine learning model that analyzes signals from various sensors, specifically looking at muscle activity, to help determine whether stroke survivors are at high or low risk for falling. The goal is to create a helpful tool that can assist healthcare providers in preventing falls, which can lead to serious injuries for stroke patients.

To participate in this study, stroke survivors need to be at least 19 years old and have experienced their stroke within the last three months. They should also have some weakness in their legs but be able to understand and follow instructions. Individuals with other serious health issues or cognitive impairments may not be eligible. Participants will undergo assessments using the new technology, and their information will help researchers understand how well this predictive tool works. This study is important because it aims to improve safety for stroke survivors and reduce their risk of falls.

Gender

ALL

Eligibility criteria

• • Stroke Participants
• Inclusion Criteria:
• • 19 years and older
• • the onset of the stroke is less than 3months ago
• • Lower extremity weakness due to stroke (MMT =\< 4 grade)
• • Cognitive ability to follow commands
• Exclusion Criteria:
• • stroke recurrence
• • other neurological abnormalities (e.g. parkinson's disease).
• • severely impaired cognition
• • serious and complex medical conditions(e.g. active cancer)
• • cardiac pacemaker or other implanted electronic system
• • Health Participants
• Inclusion Criteria:
• • 19 years and older
• • Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects
• Exclusion Criteria:
• • other neurological abnormalities (e.g. parkinson's disease).
• • severely impaired cognition
• • serious and complex medical conditions(e.g. active cancer)
• • cardiac pacemaker or other implanted electronic system