RT Journal Article
T1 Becalm: Intelligent Monitoring of Respiratory Patients
A1 Recio García, Juan Antonio
A1 Díaz Agudo, María Belén
A1 Acuaviva, Arturo
AB The Becalm project is an open and low-cost solution for the remote monitoring of respiratory support therapies like the ones used in COVID-19 patients. It is a combined architecture based on a Case-Based Reasoning (CBR) decision-making system for the remote monitoring, detection, and explanation of risk situations for respiratory patients using a low-cost non-invasive mask. This paper describes the mask and the sensors that allow remote monitoring. Then, it describes the intelligent decision-making system that detects anomalies and raises early alerts that are visualized and explained to healthcare professionals. This detection is based on the comparison of cases that represent patients using a set of static variables, plus the dynamic vector of the patient time series from sensors. The experiments reported in this paper are based on a synthetic data generator that simulates realistic patients using a synthesis process developed from the analysis of the available clinical literature. This process has been verified with real data and allows the validation of the reasoning system with noisy and incomplete data, threshold values, and life/death situations. Besides, we have evaluated three different distance metrics for the reasoning system in either optimal situations or cold-start and noisy situations. Our results demonstrate promising results and good accuracy for the proposed low-cost method to supervise COVID-19 patients for medical staff.
PB IEEE
YR 2023
FD 2023-05-16
LK https://hdl.handle.net/20.500.14352/128233
UL https://hdl.handle.net/20.500.14352/128233
LA eng
NO J. A. Recio-Garcia, B. Diaz-Agudo and A. Acuaviva, "Becalm: Intelligent Monitoring of Respiratory Patients," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 8, pp. 3806-3817, 2023.
NO BOSCH-UCM Honorary Chair on Artificial Intelligence applied to Internet of Things (https://www.ucm.es/catedrabosch)
NO PERXAI project PID2020-114596RB-C21 funded by MCIN/AEI/10.13039/501100011033
NO iSee project (CHIST-ERA-19-XAI-008, PCI2020-120720-2) funded by MCIN/AEI/10.13039/501100011033
DS Docta Complutense
RD 18 mar 2026