Noninvasive glucose monitoring using polarized light

Li, Tianxing; Bai, Derek; Prioleau, Temiloluwa; Bui, Nam; Vu, Tam; Zhou, Xia

doi:10.1145/3384419.3430720

Cited by 17 publications

(7 citation statements)

References 59 publications

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“…Existing and Planned Work: While existing work has ventured in various directions, there is a growing need for sensing health biomarkers correctly and developing ML approaches that are fast and can accurately identify these biomarkers. Researchers have focused on developing novel sensing systems that can sense various health behaviors and biomarkers (Holz and Wang, 2017 ; Bui et al, 2019 ; Bedri et al, 2020 ; Chun et al, 2020 ; Echterhoff and Wang, 2020 ; Li et al, 2020b ; Pham et al, 2020 ). Historically, most of these novel sensing techniques were tested in controlled settings, but more recently researchers are ensuring that these systems can work seamlessly in free-living settings as well.…”

Section: Exemplars Of Domain Applicationsmentioning

confidence: 99%

Applications and Techniques for Fast Machine Learning in Science

Deiana¹,

Tran²,

Agar³

et al. 2022

Front. Big Data

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In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

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Section: Exemplars Of Domain Applicationsmentioning

confidence: 99%

Applications and Techniques for Fast Machine Learning in Science

Deiana¹,

Tran²,

Agar³

et al. 2022

Front. Big Data

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“…We further study polarimetry (a.k.a. ellipsometry) founded on polarization [4,46,71,85]. Essentially, when electromagnetic waves are reflected from the boundary of a target (of a certain material), their polarization is altered by the target's properties (e.g., refractive index, thickness, and dielectric function tensor) [4].…”

Section: Methodsmentioning

confidence: 99%

SiWa: See into Walls via Deep UWB Radar

Zheng,

Chen,

Luo

et al. 2021

Preprint

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Being able to see into walls is crucial for diagnostics of building health; it enables inspections of wall structure without undermining the structural integrity. However, existing sensing devices do not seem to offer a full capability in mapping the in-wall structure while identifying their status (e.g., seepage and corrosion). In this paper, we design and implement SiWa as a low-cost and portable system for wall inspections. Built upon a customized IR-UWB radar, SiWa scans a wall as a user swipes its probe along the wall surface; it then analyzes the reflected signals to synthesize an image and also to identify the material status. Although conventional schemes exist to handle these problems individually, they require troublesome calibrations that largely prevent them from practical adoptions. To this end, we equip SiWa with a deep learning pipeline to parse the rich sensory data. With innovative construction and training, the deep learning modules perform structural imaging and the subsequent analysis on material status, without the need for repetitive parameter tuning and calibrations. We build SiWa as a prototype and evaluate its performance via extensive experiments and field studies; results evidently confirm that SiWa accurately maps in-wall structures, identifies their materials, and detects possible defects, suggesting a promising solution for diagnosing building health with minimal effort and cost. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools.

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“…Taking advantage of the multi-wavelength PPG (mW-PPG) technique, we can extract diverse information from multilayers in tissues for healthcare monitoring. Therefore, this method has recently attracted the attention of more researches for various applications such as conducting blood pressure (BP) and glucose concentration level (GCL) measurements [10]- [17].…”

Section: Introductionmentioning

confidence: 99%

Wearable Wrist Photoplethysmography for Optimal Monitoring of Vital Signs: A Unified Perspective on Pulse Waveforms

Nguyen

Chung

2022

IEEE Photonics J.

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To track vital signs on the wrist, a wearable system, named WrisTee, has been developed for remote health monitoring. Using three optical sensors with four light sources, WrisTee allows users to measure 12 photoplethysmogram (PPG) signals at three locations on the radial artery. Two types of PPG signals with opposite polarities were discovered and designated as inphase and invert-phase signals. We provided a unified viewpoint regarding their differences based on the Beer−Lambert law, and showed that both signals can be used for heart monitoring using data analyzed from a selected subject. Using reflective pulse-transition time (R-PTT) and the standard deviation of R-PTT (σR-P T T ), we proposed a method for selecting the optimal wavelength to achieve the best quality signal, thus minimizing storage requirements, power resources, and computational costs. We conducted an experiment on ten subjects to evaluate the feasibility of the proposed method. Our results demonstrated that WrisTee is capable of finding the optimal positions and wavelengths for monitoring vital signs. To automatically detect the PPG phases, six machine learning (ML) models were explored to assess their accuracy for PPG-phase classification. The experimental results show that a convolutional neural network can be the best candidate for phase classification. Hence, it can be integrated into WrisTee for noninvasive health monitoring such as heart rate, heart rate variability, or blood pressure. Our work paves a new direction in bio-signal medical researches by adopting in-phase and invert-phase PPGs for healthcare monitoring.

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Noninvasive glucose monitoring using polarized light

Cited by 17 publications

References 59 publications

Applications and Techniques for Fast Machine Learning in Science

Applications and Techniques for Fast Machine Learning in Science

SiWa: See into Walls via Deep UWB Radar

Wearable Wrist Photoplethysmography for Optimal Monitoring of Vital Signs: A Unified Perspective on Pulse Waveforms

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