Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm

TL;DR

This paper presents a non-contact RF-based method using SDRs and machine learning to classify breathing abnormalities by analyzing hand-induced wireless channel responses, offering a low-exposure alternative to chest-based methods.

Contribution

It introduces a novel hand-based RF sensing approach combined with ML for breathing abnormality detection, reducing RF exposure compared to traditional chest-based methods.

Findings

Achieved up to 88.1% accuracy with linear SVM classifier.

Demonstrated the feasibility of hand-based RF sensing for breathing monitoring.

Compared performance with chest-based RF methods, showing a trade-off between accuracy and exposure.

Abstract

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method.