Multidimensional analysis using sensor arrays with deep learning for high-precision and high-accuracy diagnosis

• URL: http://arxiv.org/abs/2211.17139v1
• Date: Wed, 30 Nov 2022 16:14:55 GMT
• Title: Multidimensional analysis using sensor arrays with deep learning for high-precision and high-accuracy diagnosis
• Authors: Julie Payette, Sylvain G.Cloutier and Fabrice Vaussenat
• Abstract summary: We demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy by feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the upcoming years, artificial intelligence (AI) is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between 0.5-2.0$^\circ$C. 800 vectors are extracted, covering a range from to 30 to 45$^\circ$C. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent (SGD) optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10$^{-4}$ on the training set and 1.22x10$^{-4}$ on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.

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