Trials Sponsors Conditions Drugs Pricing

Conditions

Fall Stroke

Keywords

Predict modelMachin leanningElectromyography

Outcome Measures

Primary Outcomes (1)

Sensitivity of the Machine Learning Model
The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified.
At the time of the single visit

Secondary Outcomes (1)

Specificity of the Machine Learning Model
At the time of the single visit

Other Outcomes (2)

Area Under the Receiver Operating Characteristic Curve
At the time of the single visit
Matthews Correlation Coefficient
At the time of the single visit

Interventions

EMG Analysis SoftwareDevice

Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.

Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.

Eligibility Criteria

Age19 Years+

Sexall

Healthy VolunteersYes

Age GroupsAdult (18-64), Older Adult (65+)

Sampling MethodProbability Sample

Study Population

The study aims to enroll approximately 80 stroke patients and 10 healthy adults to facilitate a comprehensive analysis of the EMG-based machine learning model's effectiveness.

You may qualify if:

years and older

Sponsors & Collaborators

Ministry of Trade, Industry & Energy, Republic of Korea
Governmentcollaborator
Seoul National University Hospital
Otherlead

Study Sites (1)

Seoul National University Hospital

Seoul, Jongno, 03080, South Korea

RECRUITING

junghyun kim, Ph. D.CONTACT82+1021740890

MeSH Terms

Conditions

Stroke

Condition Hierarchy (Ancestors)

Cerebrovascular DisordersBrain DiseasesCentral Nervous System DiseasesNervous System DiseasesVascular DiseasesCardiovascular Diseases

Study Officials

Woo Hyung Lee, prof
Seoul National University Hospital
PRINCIPAL INVESTIGATOR
Byung-Mo Oh, prof
Seoul National University Hospital
STUDY DIRECTOR
Han Gil Seo, prof
Seoul National University Hospital
STUDY DIRECTOR
Sung Eun Hyun, prof
Seoul National University Hospital
STUDY DIRECTOR

Central Study Contacts

JungHyun Kim, prof

CONTACT

82+1088632341 kiking0@naver.com

Study Design

Study Type: observational
Observational Model: OTHER
Time Perspective: CROSS SECTIONAL
Sponsor Type: OTHER
Responsible Party: SPONSOR

Study Record Dates

First Submitted

April 15, 2024

First Posted

April 23, 2024

Study Start

May 20, 2024

Actual (reported)

Primary Completion

March 12, 2025

Actual (reported)

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

Brief Summary

Registry Overview

Trial Relationships

Study Timeline

First Submitted

First Posted

Study Start

Primary Completion

Study Completion

Conditions

Keywords

Outcome Measures

Primary Outcomes (1)

Secondary Outcomes (1)

Other Outcomes (2)

Interventions

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

MeSH Terms

Conditions

Condition Hierarchy (Ancestors)

Study Officials

Central Study Contacts

Study Design

Study Record Dates

You may not qualify if:

Data Sharing

Source record

Locations

Brief Summary

Registry Overview

Trial Relationships

Related Scientific Literature

Study Timeline

First Submitted

First Posted

Study Start

Primary Completion

Study Completion

Conditions

Keywords

Outcome Measures

Primary Outcomes (1)

Secondary Outcomes (1)

Other Outcomes (2)

Interventions

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

MeSH Terms

Conditions

Condition Hierarchy (Ancestors)

Study Officials

Central Study Contacts

Study Design

Study Record Dates

You may not qualify if:

Data Sharing

Source record

Locations