Version 1
: Received: 26 September 2024 / Approved: 27 September 2024 / Online: 29 September 2024 (05:52:22 CEST)
How to cite:
Jimeno Sánchez-Patón, P.; Fuentes Moro, D.; Luengo López, I.; Ortiz Canepa, J. I. AIVA: A Mobile Application for AI-Based Vital Signs Assessment Based on Video from Smartphone Camera. Preprints2024, 2024092255. https://doi.org/10.20944/preprints202409.2255.v1
Jimeno Sánchez-Patón, P.; Fuentes Moro, D.; Luengo López, I.; Ortiz Canepa, J. I. AIVA: A Mobile Application for AI-Based Vital Signs Assessment Based on Video from Smartphone Camera. Preprints 2024, 2024092255. https://doi.org/10.20944/preprints202409.2255.v1
Jimeno Sánchez-Patón, P.; Fuentes Moro, D.; Luengo López, I.; Ortiz Canepa, J. I. AIVA: A Mobile Application for AI-Based Vital Signs Assessment Based on Video from Smartphone Camera. Preprints2024, 2024092255. https://doi.org/10.20944/preprints202409.2255.v1
APA Style
Jimeno Sánchez-Patón, P., Fuentes Moro, D., Luengo López, I., & Ortiz Canepa, J. I. (2024). AIVA: A Mobile Application for AI-Based Vital Signs Assessment Based on Video from Smartphone Camera. Preprints. https://doi.org/10.20944/preprints202409.2255.v1
Chicago/Turabian Style
Jimeno Sánchez-Patón, P., Inmaculada Luengo López and José Ignacio Ortiz Canepa. 2024 "AIVA: A Mobile Application for AI-Based Vital Signs Assessment Based on Video from Smartphone Camera" Preprints. https://doi.org/10.20944/preprints202409.2255.v1
Abstract
Heart rate and blood pressure, along with body temperature and respiratory rate, are two of the four parameters used to assess the state of bodily functions, as both measures are indicators of heart activity. Therefore, these measurements become a basic procedure in patient diagnosis, necessitating techniques that can obtain them accurately and reliably, while being as non-intrusive as possible for the patient. Using video data, these two vital signs can be determined at any time and place. To achieve this, artificial intelligence techniques such as machine learning and deep learning 6 can be applied to overcome the main limitations of conventional remote photoplethysmography (rPPG). This document explains the technical and methodological aspects involved, including the acquisition and preprocessing of video data, the extraction of the rPPG signal, and its subsequent processing, as well as the artificial intelligence model for heart rate estimation and the mathematical modeling for blood pressure estimation. We compared the results for heart rate and blood pressure extracted from videos recorded by a basic smartphone front-camera to a blood pressure monitor and achieved results with reasonable accuracy and correlation. These results are part of the AIVA project, grant agreement 2021/C005/00145071. This project has been funded by Red.es through the Spanish 2021 call for grants aimed at research and development projects in artificial intelligence and other digital technologies.
Keywords
Heart Rate; Blood Pressure; Deep Learning; Remote Photoplethysmography (rPPG)
Subject
Public Health and Healthcare, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
