Clinical evaluation of a polarization-based optical noninvasive glucose sensing system

Abstract

Diabetes affects millions in the US, causing elevated blood glucose levels that could lead to complications like kidney failure and heart disease. Recent development of continuous glucose monitors has enabled a minimally invasive option, but the discomfort and social factors highlight the need for noninvasive alternatives in diabetes management. We propose a portable noninvasive glucose sensing system based on the glucose's optical activity property which rotates linearly polarized light depending on its concentration level. To enable a portable form factor, a light trap mechanism is used to capture unwanted specular reflection from the palm and the enclosure itself. We fabricate four sensing prototypes and conduct a 363-day multi-session clinical evaluation in real-world settings. 30 participants are provided with a prototype for a 5-day home monitoring study, collecting on average 8 data points per day. We identify the error caused by differences between the sensing boxes and the participants' improper usage. We utilize a machine learning pipeline together with Bayesian Ridge Regressor models and multiple-step data processing techniques to deal with the noisy data. Over 95% of the predictions fall within Zone A (clinically accurate) or B (clinically acceptable) of the Consensus Error Grid with a 0.24 mean absolute relative differences.

Fig. 1

System overview. (a) Palm’s placement on the device. (b) Screenshot of Smartphone Application. (c) Sensing Box schematic. (d) Cross-section of the Sensing Box. (e) End-to-end glucose level estimation pipeline.

Fig. 2

System concept. (a) A basic polarimetry setup to measure optical rotation caused by glucose. (b) Interaction of polarized light with the human skin.

Fig. 3

The predicted glucose values generated using three random seeds are plotted against the reference glucose values onto three CEGs. The points in green are predictions that fall in Zone A, which are considered clinically accurate. The points in blue are those in Zone B, which are clinically acceptable. The predictions that fall into Zone C are marked as purple, which predicted a higher or lower glucose value with respect to the reference value. The percentage of predictions that fall into Zone A or B are 95.29%, 95.14%, and 95.29%, respectively. Conversely, 4.71%, 4.86%, and 4.71% of predictions fall into Zone C. There are no predictions in Zone D and E.

Fig. 4

The performance of each participant. Each bar corresponds to the averaged MARD from the trajectories for a single participant. The performances are sorted in an ascending order.

Fig. 5

The performances of the system regarding different (a) sensing boxes, (b) genders, and (c) races. Each bar corresponds to a trajectory, and they are grouped by (a) sensing box ID, (b) genders, and (c) races.

Fig. 6

The improper use dataset and the main dataset are plotted after applying PCA in a 2D feature space. The points in light blue are the noisy data that we collected in the improper use dataset and the red points are filtered out, leaving the dark blue data points in the main dataset. PC1 and PC2 denote the top-2 principal components.