@article{10902/31774,
year = {2024},
url = {https://hdl.handle.net/10902/31774},
abstract = {This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system?s effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.},
organization = {This work is part of the projects 2020/INN/21 funded by Gobierno de Cantabria; PID2019-107270RB-C21, PDC2021-121172-C21 and TED2021-130378B-C21 project funded by MCIN/AEI/ 10.13039/501100011033, FEDER, and EU NextGenerationEU/PRT. J.F.A. received funding from Ministerio de Ciencia, Innovación y Universidades of Spain under Juan de la Cierva-Incorporación grant.},
publisher = {MDPI},
publisher = {Sensors, 2024, 24(1), 129},
title = {Non-contact thermal and acoustic sensors with embedded artificial intelligence for point-of-care diagnostics},
author = {Rodríguez Cobo, Luis and Reyes González, Luis Rafael and Algorri Genaro, José Francisco and Díez del Valle Garzón, Sara and García García, Roberto and López Higuera, José Miguel and Cobo García, Adolfo},
}