Emergent

84% of drivers in medical crises crash. Emergent’s AI system detects anomalies, alerts 911, & guides users, saving lives. Scalable for all emergencies, it makes accessible care a life-saving reality.

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What Inspired Us: The alarming statistic that 84% of drivers experiencing medical emergencies end up crashing inspired us to act. Knowing the devastating impact on lives and families, we sought a solution that could not only safeguard drivers but also help anyone facing unexpected medical crises. Our mission was to bridge the gap between health monitoring and immediate care, making emergency assistance accessible to all.

What We Learned: Through this journey, we learned the importance of early detection and real-time intervention in preventing fatal outcomes. Understanding how vital sign changes can indicate emergencies like seizures, diabetic reactions, or cardiac events sharpened our approach. We also gained insights into the intricacies of integrating wearable tech, privacy concerns, and the significance of designing for scalability and inclusivity.

How We Built It: We developed an AI-driven platform utilizing wearable health data and Fetch.ai’s uAgent framework. Our dual-mode system combines user-initiated and automated responses. The health monitoring agent detects anomalies, while the emergency response agent communicates with caregivers and emergency services. This architecture was powered by a Flask backend and deployed on Agentverse for secure data exchange.

Challenges We Faced: Building this project came with several challenges. Ensuring the accurate detection of anomalies in diverse medical conditions required robust machine learning models and extensive testing. Designing interactions that were both sensitive and effective during high-stress situations was another significant hurdle, as we needed to balance urgency with user comfort. Integrating multiple technologies, including wearable devices, Fetch.ai’s uAgent framework, and the Flask backend, required careful synchronization to ensure real-time responses. Lastly, we faced difficulties in simulating real-world medical emergencies for testing, which required creative approaches to validate the system's reliability and effectiveness.

Each of these challenges pushed us to innovate, improve, and refine our solution, ultimately making it both robust and impactful.

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