Status
Conditions
Treatments
Study type
Funder types
Identifiers
About
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.
Full 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:
Participants:
• Sample Size: 80 stroke patients and 10 healthy adults to establish baseline EMG readings.
Interventions:
• Participants will undergo EMG signal collection from key lower limb muscles while performing standardized movements.
Outcome Measures:
Data Collection:
Statistical Analysis:
Enrollment
Sex
Ages
Volunteers
Inclusion and exclusion criteria
Stroke Participants
Inclusion Criteria:
Exclusion Criteria:
Health Participants
Inclusion Criteria:
Exclusion Criteria:
Loading...
Central trial contact
JungHyun Kim, prof
Data sourced from clinicaltrials.gov
Clinical trials
Research sites
Resources
Legal