Remote laser-speckle sensing of heart sounds for health assessment and biometric identification

TL;DR

This paper introduces a contactless laser speckle sensing technique combined with machine learning for remote heart sound identification, outperforming traditional stethoscopes and enabling remote cardiovascular health monitoring.

Contribution

It presents a novel, contactless laser-based method for heart sound analysis that surpasses standard digital stethoscopes in biometric identification accuracy.

Findings

Outperforms digital stethoscope in biometric identification

Effective on data collected on different days

Enables remote cardiovascular health monitoring

Abstract

Assessment of heart sounds is the cornerstone of cardiac examination, but it requires a stethoscope, skills and experience, and a direct contact with the patient. We developed a contactless, machine-learning assisted method for heart-sound identification and quantification based on the remote measurement of the reflected laser speckle from the neck skin surface in healthy individuals. We compare the performance of this method to standard digital stethoscope recordings on an example task of heart-beat sound biometric identification. We show that our method outperforms the stethoscope even allowing identification on the test data taken on different days. This method might allow development of devices for remote monitoring of cardiovascular health in different settings.