design and implementation of an intelligent iot-enabled wearable ecg device for continuous cardiac monitoring

Introduction: cardiovascular diseases, especially arrhythmias, require continuous monitoring to ensure timely intervention. although the elderly are more susceptible, individuals of all ages could benefit from real-time cardiac monitoring [1-5]. this study proposes the development of an iot-enabled wearable ecg gadget for continuous cardiac monitoring to assist in the early detection of cardiac arrhythmias using ai. methods and materials: as illustrated in fig.1, the proposed system employs a wearable ecg gadget to continuously collect data from the user. this realtime information is wirelessly transmitted to the user’s smartphone, serving as the iot gateway, and subsequently sent to the remote server for ai-based analysis. to differentiate between normal and arrhythmic ecg patterns, a deep learning model has been developed and trained with a benchmark dataset in tensorflow. the proposed system consists of four key components: a) a wearable ecg device for continuous data acquisition, b) a wireless on-body area network for real-time data transmission to the gateway, c) an iot platform for remote patient monitoring, and d) an ai module for arrhythmia diagnosis. results: the proposed wearable system integrates iot and ai technologies to enable continuous monitoring and intelligent management of cardiac health. it is expected to enhance early diagnosis and timely interventions significantly. physicians and healthcare teams can monitor and manage patients' cardiac health remotely at any time, with the ability to track progress over time. the ai module serves as a clinical decision support tool, providing accurate real-time detection of arrhythmias. the effectiveness of the ai model will be evaluated by comparing its findings and recommendations to those of medical experts. conclusion and discussion: this study presents an iot-enabled wearable ecg device designed for continuous cardiac monitoring and early detection of arrhythmias using ai. the system collects real-time data from user by a wearable ecg gadget, transmits this information to a remote server based on an iot platform, and finally employs a deep learning model for arrhythmia diagnosis. by integrating iot and ai, the device aims to enhance early diagnosis and enable remote monitoring by healthcare providers, improving care for both elderly patients and those at risk of heart disease.