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

NCT06380049 | Recruiting DMC

• 2 other identifiers: 072024211020240012366
• Study Type: observational
• Target: 90
• Locations: 1 country
• Sites: 1

Brief Summary

The study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize patients into high-risk or low-risk fall categories. The innovative approach hopes to offer a predictive tool that enhances preventative strategies in clinical settings, potentially reducing fall-related injuries in stroke survivors.

Conditions

Stroke; Fall

Keywords

Predict model; Machin leanning; Electromyography

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 Software DEVICE

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

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability 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
• the onset of the stroke is less than 3months ago
• Lower extremity weakness due to stroke (MMT =\< 4 grade)
• Cognitive ability to follow commands

You may not qualify if:

• 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
• years and older
• Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects
• 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

Sponsors & Collaborators

• Seoul National University Hospital: lead
• Ministry of Trade, Industry & Energy, Republic of Korea: collaborator

Study Sites (1)

Seoul National University Hospital

Seoul, Jongno, 03080, South Korea

RECRUITING

MeSH Terms

Conditions

Stroke

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular 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

• Hyunmi Oh, prof

  National Traffic Injury Rehabilitation Hospital

  STUDY DIRECTOR

• Sumin Oh, B.S.

  National Traffic Injury Rehabilitation Hospital

  STUDY DIRECTOR

• SO YEON JEON, B.S.

  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
• Primary Completion: March 12, 2025
• Study Completion: April 28, 2026
• Last Updated: June 2, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)