Antonio Alarcón-Paredes
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Victor Francisco-García
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Iris P. Guzmán-Guzmán
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Jessica Cantillo-Negrete
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René E. Cuevas-Valencia
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Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to prick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes.
In vivo Microscopic Photoacoustic Spectroscopy for Non-Invasive Glucose Monitoring Invulnerable to Skin Secretion Products
