Virtual Booth

Introduction 

Epilepsy is a chronic neurological disorder marked by unpredictable seizures affecting millions worldwide. Traditional management relies on clinical visits and patient-reported seizure logs, often delayed or inaccurate, reducing timely intervention and patient safety. Wearable sensors and mobile-health platforms offer continuous, real-time monitoring and insights. Current systems face low detection accuracy, user discomfort, high false alarms, and integration challenges. This study develops a EpiCare Connect which it combining wearable sensors, a mobile app, and cloud analytics, evaluates seizure-detection performance, assesses usability, and examines effects on medication adherence and overall safety. 

Methodology 

A mixed-methods design was employed with a four-week pilot trial. Participants were recruited via purposive sampling from a neurology clinic. The system included a wearable sensor for physiological signals, a mobile app for alerts, and cloud-based analytics. Data included sensor readings, alert timestamps, and clinically confirmed seizures. Usability was evaluated via questionnaires. Quantitative analysis computed detection accuracy, sensitivity, specificity, and false alarms. Limitations include the small sample and short monitoring period.