NCT06380049

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

Development and Validation of a Machine Learning-based Model to Predict a High-risk Group for Falls Using Multi-sensor Signals in Stroke Patients

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 90 (estimated)
• Sponsor: Seoul National University Hospital · Academic / Other
• Sex: All
• Age: 19 Years
• Healthy volunteers: Accepted

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

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2024-05-20
• Primary completion: 2025-03-12
• Completion: 2026-04-28
• First posted: 2024-04-23
• Last updated: 2025-06-02

Locations

1 site across 1 country: South Korea