Fall, Stroke
Conditions
Keywords
Predict model, Machin leanning, Electromyography
Brief 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.
Interventions
DEVICEEMG Analysis Software
Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.
Sponsors
Ministry of Trade, Industry & Energy, Republic of Korea
Seoul National University Hospital
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
19 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
Stroke Participants Inclusion Criteria: * 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
Exclusion criteria
* 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 Inclusion Criteria: * 19 years and older * Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of the Machine Learning Model | At the time of the single visit | 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
| Measure | Time frame | Description |
|---|---|---|
| Specificity of the Machine Learning Model | At the time of the single visit | Specificity measures the proportion of actual negatives that are correctly identified. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve | At the time of the single visit | 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 | At the time of the single visit | 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. |
Countries
South Korea
Contacts
Primary ContactJungHyun Kim, prof
Outcome results
None listed