A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography

TL;DR

This paper introduces MIBINET, a CNN-based framework for real-time, contactless heart rate monitoring using millimeter wave radar signals, demonstrating high accuracy and versatility across multiple signal modalities.

Contribution

The paper presents a novel lightweight CNN model that accurately estimates heart rate from mm-wave radar signals and is adaptable to other physiological signal types.

Findings

MIBINET achieves high accuracy in heart rate estimation from mm-wave signals.

The framework is effective across multiple signal modalities such as PCG, ECG, and PPG.

Experimental results outperform current state-of-the-art methods.

Abstract

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural network