CalmPulse: A Wearable Device and ML-Based Framework for Real-Time Panic Attack Detection and Intervention

CalmPulse has been introduced in this paper as a cloud-based wearable health monitoring and emergency alert system, which has been designed using MAX30102 sensor, XIAO ESP32-C3 microcontroller, Firebase Realtime Database, and Android mobile application. The framework facilitates the continuous monitoring of physiological parameters such as heart rate and blood oxygen saturation in connection with cloud communication and availability from anywhere.   The decision tree-based risk classification has been performed in the proposed framework as a smart risk assessment module, wherein the different risk levels can be classified based on physiological states. An emergency alerting module has been implemented in order to respond to emergencies effectively. From the experimentally conducted evaluations, it has been shown that the proposed framework is able to provide reliable monitoring of physiological parameters, effective communication with cloud, responsiveness of mobile application, and generation of alerts successfully. It has been observed that the proposed framework is an economical, scalable, and viable approach to wearable health applications. The combination of sensing, communication, cloud, and mobile technologies in one framework proves the viability of IoT-enabled health care systems in preventive health care and remote patient monitoring.

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