Photoacoustics on the go: An Embedded Photoacoustic Sensing Platform

TL;DR

This paper introduces a compact, low-cost embedded photoacoustic sensing platform that combines optical specificity with ultrasound depth, enabling non-invasive, real-time biomarker monitoring for health applications.

Contribution

The authors develop a novel low-power microcontroller-based photoacoustic transmitter, analyze its performance, and demonstrate its capability to detect glucose and hemoglobin changes, advancing portable health monitoring technology.

Findings

Successfully detected glucose levels in controlled solutions.

Demonstrated detection of deoxygenated hemoglobin.

Achieved comparable performance to commercial systems.

Abstract

Several centimeters below the skin lie multiple biomarkers, such as glucose, oxygenation, and blood flow. Monitoring these biomarkers regularly and in a non-invasive manner would enable early insight into metabolic status and vascular health. Currently, there are only a handful of non-invasive monitoring systems. Optical methods offer molecular specificity (i.e., multi-biomarker monitoring) but have shallow reach (a few millimeters); ultrasound penetrates deeper but lacks specificity; and MRI is large, slow, and costly. Photoacoustic (PA) sensing combines the best of optical and ultrasound methods. A laser transmitter emits pulses that are absorbed by different molecules, providing specificity. These light pulses generate pressure changes that are captured by an ultrasound receiver, providing depth. Photoacoustic sensing is promising, but the current platforms are bulky, complex, and costly. We propose the first embedded PA platform. Our contributions are fourfold. First, inspired by LiDAR technology, we propose a novel transmitter that emits pulses similar to those in the state-of-the-art (SoA), but instead of using high-voltage sources and complex electronic interfaces, we use a simple low-power microcontroller (MCU). Second, we carry out a thorough analysis of our custom transmitter and a commercial system. Third, we build a basic ultrasound receiver that is able to process the faint signal generated by our transmitter. Lastly, we compare the performance of our platform against a SoA commercial system, and show that we can detect glucose and (de)oxygenated hemoglobin in two controlled solution studies. The resulting signal characteristics indicate a plausible path toward noninvasive, real-time, at-home sensing relevant to diabetes care. More broadly, this platform lays the groundwork for translating the promise of PA sensing into a broader practical reality.