About this trial
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 criteria
This trial accepts healthy volunteersQualifiers
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
Disqualifiers
stroke recurrence
other neurological abnormalities (e.g. parkinson's disease).
severely impaired cognition
serious and complex medical conditions(e.g. active cancer)
Trial population
The study aims to enroll approximately 80 stroke patients and 10 healthy adults to facilitate a comprehensive analysis of the EMG-based machine learning model's effectiveness.
Trial design
Other
Cross-sectional
Treatments tested in this trial
EMG Analysis Software
DeviceSurface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.
Treatment groups
Trial outcomes
Primary outcomes
Sensitivity of the Machine Learning Model
The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified.
Secondary outcomes
Specificity of the Machine Learning Model
Specificity measures the proportion of actual negatives that are correctly identified.
Other outcomes
Area Under the Receiver Operating Characteristic Curve
This is a performance measurement for classification problems at various threshold settings. ROC is a probability curve, and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes.
Matthews Correlation Coefficient
The MCC is used in machine learning as a measure of the quality of binary classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes.
Sponsors and contacts
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Seoul National University Hospital
Lead sponsor
Ministry of Trade, Industry & Energy, Republic of Korea
Collaborator