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Many serious health conditions, such as atrial fibrillation (AF), neuropathy, muscle disorders, and sleep-related neurological diseases, often go undiagnosed until complications arise. To address these challenges, this paper presents an advanced health monitoring system that integrates a multifunctional 4-in-1 electrogram sensor capable of measuring muscle activity using Electromyography (EMG), eye movement using Electrooculography (EOG), brain activity using Electroencephalography (EEG), and heart rhythm using Electrocardiography (ECG), along with a body temperature sensor, into a compact and wearable device at low cost. The device leverages the ESP32 Wi-Fi module to process and enable seamless data transmission to a Message Queuing Telemetry Transport (MQTT) cloud platform, ensuring secure, efficient, and scalable storage and analysis of collected health data. The system uses multiple pre-trained CNN models, each specialized in detecting specific diseases. Tests show an average accuracy of 90.3% making it a cost effective and efficient solution.

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IoT-Based Health Monitoring System Using Four in One Electrogram Sensor

  • 会议论文

  • 发表日期：

    2026年

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  • Jigme Nidup,
  • Adithya Gattadi,
  • K. Naresh

摘要

Many serious health conditions, such as atrial fibrillation (AF), neuropathy, muscle disorders, and sleep-related neurological diseases, often go undiagnosed until complications arise. To address these challenges, this paper presents an advanced health monitoring system that integrates a multifunctional 4-in-1 electrogram sensor capable of measuring muscle activity using Electromyography (EMG), eye movement using Electrooculography (EOG), brain activity using Electroencephalography (EEG), and heart rhythm using Electrocardiography (ECG), along with a body temperature sensor, into a compact and wearable device at low cost. The device leverages the ESP32 Wi-Fi module to process and enable seamless data transmission to a Message Queuing Telemetry Transport (MQTT) cloud platform, ensuring secure, efficient, and scalable storage and analysis of collected health data. The system uses multiple pre-trained CNN models, each specialized in detecting specific diseases. Tests show an average accuracy of 90.3% making it a cost effective and efficient solution.