Key result
Novel CNN-based monitoring system enhances arrhythmia detection in deep sea manned submersibles.
Why the study?
Continuous health surveillance and swift anomaly detection triggering rescue alerts are paramount for individuals in deep sea manned submersibles.
This project demonstrates a novel machine learning-based health monitoring system for extreme remote conditions like deep sea submersibles.
Hypothesis-generating for arrhythmia monitoring in extreme environments; prospective validation required before clinical use.
This project pioneers a robust health monitoring system for individuals in Deep Sea Manned Submersibles, spanning both Human Occupied Vehicles (HOV) and Remotely Controlled Vehicles (RCV). Continuous health surveillance is paramount, with swift anomaly detection triggering rescue alerts. Our system surpasses previous models by leveraging real datasets for CNN algorithms and images for Pan Tompkins algorithms. Comparative evaluation of diverse algorithms, including Pan Tompkins and CNN, enhances arrhythmia detection and classification accuracy. Our hardware setup integrates an AD8232 sensor with an ESP32 running trained ML hex files, bolstered by a combined notch and low-pass filter for superior noise reduction. Additional SPo2, HR, temperature, and humidity sensors augment data collection. Real-time data transmission to a web server enables comprehensive analysis. This project underscores breakthroughs in machine learning and healthcare technologies, transforming remote health monitoring in extreme conditions.
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M. et al. (2024) studied Health monitoring in extreme conditions (submersibles). Health monitoring system (AD8232 sensor, ESP32, CNN algorithms) vs. Previous models was evaluated on Arrhythmia detection and classification accuracy. A novel health monitoring system integrating CNN algorithms, AD8232 sensors, and ESP32 microcontrollers enhances arrhythmia detection for individuals in deep sea manned submersibles.
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