International Journal of Drug Delivery Technology · May 14, 2026
AI Powered Wearable System For Pre Stroke Detection
By S MP, S MS, B MA et al.
This paper introduces a novel, intelligent wearable platform designed for the early prognostication of cerebrovascular accident (stroke) risk through continuous, multi-parametric physiological surveillance. The system is conceptualized to address the significant lag time between the onset of pathological physiological changes and clinical diagnosis, a primary factor contributing to the severe morbidity and mortality associated with stroke globally. It employs a synergistic integration of non-invasive biosensors to concurrently monitor an ensemble of biomarkers, including cardiac electrophysiology (ECG), heart rate variability (HRV), peripheral capillary oxygen saturation (SpO₂), and tri-axial kinematic data for postural analysis. At its core, an optimized embeddedsystem-on-chip (SoC) performs real-time digital signal processing—encompassing adaptive filtering, feature extraction, and spectral analysis—followed by the application of a lightweight anomaly detection algorithm to identify deviations indicative of ischemic or hemorrhagic precursors. The device's architecture prioritizes a low-power profile and ergonomic design to facilitate seamless, long-term deployment in unstructured, ambulatory settings. Preliminary validation involving controlled pilot studies demonstrates a high degree of accuracy in classifying pre-stroke events, underscoring the system's potential as a robust tool for pre- emptive healthcare. By enabling timely medical intervention, this technology aims to substantially reduce diagnostic latency, improve patient outcomes, and decrease the overall burden on healthcare infrastructure.