Ignite Creation Date:
2024-05-06 @ 8:28 PM
Last Modification Date:
2024-10-26 @ 3:28 PM
Study NCT ID:
NCT06398431
Status:
COMPLETED
Last Update Posted:
2024-05-16
First Post:
2023-12-21
Brief Title:
Validating Wireless Gait Sensor for Elderly Fall Risk Classification
Sponsor:
Pusan National University Yangsan Hospital
Organization:
Pusan National University Yangsan Hospital
Study Overview
Official Title:
A Study on Validation of Gait Analysis Wireless Small Inertial Sensor and Diagnostic Machine Learning Model for Classification of Elderly Fall Risk Group
Status:
COMPLETED
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The walking status of elderly patients over 65 years of age in the hospital will be verified through political analysis and objective fall risk assessment through wireless inertial sensors and diagnostic machine learning models and based on the results As investigators providing a foundation for the objective evaluation of the risk of falling patients by nurses in general wards in the future
Detailed Description:
Currently in the case of general clinical wards in Korea the evaluator who assesses the risk of falling during the patients hospitalization changes every time and the evaluation of fall risk differs for the same patient depending on the subjectivity of the evaluator Hence evaluating falls requires assessing the patients walking based on consistent criteria Through walking analysis with a wireless small inertial sensor there is an expectation that the incidence of fall risk will decrease When analyzing walking to classify fall risk groups quantitative evaluation should be applied for stride length gait speed step width cadence and gait cycle but currently fall assessments taking this into account are not properly conducted Therefore it is necessary to prepare and apply quantitative standards for fall evaluation through walking analysis through wireless small inertial sensors and data machine learning to classify the risk of falling in elderly hospitalized patients
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None