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

NCT ID: NCT06380049

Last Updated: 2025-06-02

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 90 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-05-20
• Study Completion Date: 2026-04-28

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.

Detailed Description

Objective: The primary objective is to develop and validate a machine learning-based model that uses multi-sensor (EMG) signals to identify stroke patients at high risk of falls. This model aims to improve on traditional fall risk assessments which rely heavily on physical assessments and patient history.

Study Design: This is a prospective, multicenter, open-label, confirmatory clinical trial. It involves collecting EMG data from stroke patients and applying machine learning techniques to predict fall risk. The study will compare the predictive accuracy of the machine learning model against conventional fall risk assessment tools.

Methods:

1. Participants:

• Sample Size: 80 stroke patients and 10 healthy adults to establish baseline EMG readings.

2. Interventions:

• Participants will undergo EMG signal collection from key lower limb muscles while performing standardized movements.

3. Outcome Measures:

• Primary Outcome: Sensitivity and specificity of the machine learning model in predicting high-risk fall patients.
• Secondary Outcomes: Comparison of the machine learning model's predictive performance with traditional fall risk assessment tools (e.g., Berg Balance Scale).

Data Collection:

• EMG sensors will be attached to the patients' muscles of the lower limbs. Sensors will record muscle activity during movement, which will then be analyzed using the machine learning model.
• The predictive model will be trained using features extracted from the EMG signals, and its performance will be validated against actual fall incidents reported during the follow-up period.

Statistical Analysis:

• The machine learning model's efficacy will be measured through its sensitivity (ability to correctly identify high-risk patients) and specificity (ability to correctly identify low-risk patients).
• Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) statistics will be used to assess model performance.

Conditions

Stroke; Fall

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: CROSS_SECTIONAL

Interventions

EMG Analysis Software

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

Intervention Type: DEVICE

Eligibility Criteria

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

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

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

• 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

• Minimum Eligible Age: 19 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Ministry of Trade, Industry & Energy, Republic of Korea

OTHER_GOV

Sponsor Role: collaborator

Seoul National University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Woo Hyung Lee, prof

Role: PRINCIPAL_INVESTIGATOR

Seoul National University Hospital

Byung-Mo Oh, prof

Role: STUDY_DIRECTOR

Seoul National University Hospital

Han Gil Seo, prof

Role: STUDY_DIRECTOR

Seoul National University Hospital

Sung Eun Hyun, prof

Role: STUDY_DIRECTOR

Seoul National University Hospital

Hyunmi Oh, prof

Role: STUDY_DIRECTOR

National Traffic Injury Rehabilitation Hospital

Sumin Oh, B.S.

Role: STUDY_DIRECTOR

National Traffic Injury Rehabilitation Hospital

SO YEON JEON, B.S.

Role: STUDY_DIRECTOR

Seoul National University Hospital

Locations

Seoul National University Hospital

Seoul, Jongno, South Korea

Site Status: RECRUITING

Countries

South Korea

Central Contacts

JungHyun Kim, prof

Role: CONTACT

kiking0@naver.com

82+1088632341

Facility Contacts

junghyun kim, Ph. D.

Role: primary

82+1021740890

Other Identifiers

20240012366

Identifier Type: OTHER

Identifier Source: secondary_id

0720242110

Identifier Type: -

Identifier Source: org_study_id