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
Seoul National University Hospital
90 participants
May 20, 2024
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria7
- Stroke Participants
- 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
- years and older
- Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects
Exclusion Criteria10
- 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
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Interventions
Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT06380049