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Created an innovative stroke detection machine learning algorithm that utilizes variations in physiological indicators and higher-order derivatives of PPG signals. The designed device plays a crucial role in assisting stroke patients, ensuring they reach the hospital within the critical golden window for optimal medical intervention.

The social impact of the project is centered around addressing a critical healthcare issue: strokes. By introducing a wearable device capable of detecting and alerting users to the incidence of strokes based on variations in photoplethysmography (PPG) signals, the aim is to contribute significantly to reducing severe disabilities and mortality rates associated with strokes. The device's early detection capabilities enable timely medical intervention, ensuring that individuals experiencing a stroke can receive prompt medical attention within the golden window. This timely response is crucial in minimizing the long-term consequences of strokes, potentially leading to a positive impact on public health outcomes by decreasing the severity of disabilities and lowering mortality rates associated with this serious medical condition.