An edge AIoT system for non-invasive biological indicators estimation and continuous health monitoring using PPG and ECG signals

Abstract

This paper presents the design and implementation of an artificial intelligence of things (AIoT)-based system that integrates deep learning and edge computing for real-time non-invasive health monitoring, focusing on the estimation of mean arterial pressure (MAP) alongside vital parameters such as heart rate (HR), blood oxygen saturation (SpO₂), and body temperature. Photoplethysmography (PPG) and electrocardiography (ECG) signals are acquired using low-power MAX30102 and AD8232 sensors, preprocessed with lightweight digital filters, and processed through a 1D convolutional neural network (CNN) deployed on a SEEED Studio XIAO ESP32S3 microcontroller. The model trained using the cuff-less blood pressure estimation dataset, achieved a mean absolute error (MAE) of 2.51 mmHg on the embedded microcontroller and 2.93 mmHg when validated against a standard blood pressure monitor. Experimental results demonstrate high accuracy, achieving a MAE below 5 mmHg, thereby meeting the AAMI and British Hypertension Society (BHS) Grade A standards for blood pressure measurement. The system achieves real-time inference with an average latency of 16 ms and efficient memory utilization, ensuring suitability for wearable and embedded devices. Physiological data are transmitted via Wi-Fi to a Firebase cloud platform and visualized through a cross-platform mobile application. The proposed system demonstrates strong potential for remote healthcare applications, particularly in continuous monitoring and early health risk detection.