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

NCT06380049 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 90

Last updated 2025-06-02

No results posted yet for this study

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

Interventions

DEVICE

EMG Analysis Software

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

Sponsors & Collaborators

  • Ministry of Trade, Industry & Energy, Republic of Korea

    collaborator OTHER_GOV

  • Seoul National University Hospital

    lead OTHER

Principal Investigators

  • Woo Hyung Lee, prof · Seoul National University Hospital

  • Byung-Mo Oh, prof · Seoul National University Hospital

  • Han Gil Seo, prof · Seoul National University Hospital

  • Sung Eun Hyun, prof · Seoul National University Hospital

  • Hyunmi Oh, prof · National Traffic Injury Rehabilitation Hospital

  • Sumin Oh, B.S. · National Traffic Injury Rehabilitation Hospital

  • SO YEON JEON, B.S. · Seoul National University Hospital

Eligibility

Min Age
19 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-05-20
Primary Completion
2025-03-12
Completion
2026-04-28

Countries

  • South Korea

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06380049 on ClinicalTrials.gov