Problem Statement: Early Identification of Silent Stroke Risk Using Multi-Modal Wearable Monitoring
Silent strokes occur without obvious clinical symptoms, yet they contribute significantly to long-term neurological damage, cognitive decline, and increased risk of major stroke. Current detection methods rely on imaging techniques such as MRI, which are not suitable for continuous monitoring and are typically used only after symptoms or complications arise.
There is a critical need for an accessible, non-invasive system that can continuously monitor subtle, early indicators associated with silent stroke risk in daily life.
This project addresses the gap by proposing a multi-modal wearable system integrated with external sensing capabilities. The prototype combines a smartwatch-based device to monitor pulse and movement patterns with a connected camera/microphone system to analyze facial asymmetry and vocal changes. By evaluating parameters such as irregular pulse, micro-imbalances in movement, facial muscle deviations, and speech variations, the system identifies abnormal patterns that may indicate elevated risk of silent stroke or neurological impairment.
The system provides real-time alerts and encourages early medical evaluation, aiming to reduce undetected brain damage and improve preventive healthcare, particularly for high-risk populations such as elderly individuals and patients with diabetes or hypertension.
