Santé au volant et surveillance respiratoire

Résumé :
This research aims to contribute to enhancing road safety through the development and exploration of an intelligent wristband-based health monitoring solution for car drivers. It focuses on using various sensors, such as the photoplethysmogram (PPG) and an accelerometer, to accurately estimate the drivers’ heart rate. The primary goal was to create a robust and accurate model capable of real-time heart rate estimation from PPG signals, with the potential to improve the effectiveness of Internet of Medical Things (IoMT) applications in the healthcare sector. The study delves into the multiple processing steps involved in improving the quality of data to make it suitable for efficient processing by the deep learning model, encompassing data analysis, signal interpretation, and applying diverse techniques such as filters, data shifting, and data manipulation. The research integrated the leave-one-session-out (LOSO) cross-validation technique for model training and evaluation alongside fine-tuning hyperparameters to optimize model performance and efficiency. The achieved Mean Absolute Error (MAE) of 3.450 ± 1.324 bpm and Mean Squared Error (MSE) of 69.50 ± 93.57 bpm2 represent notable outcomes, resulting in a 54.9% improvement in MAE from the original study. Additionally, the research integrated the model into a user-friendly mobile application, visually presenting the results and enabling users to examine their health status in real-time. These findings highlight the significance of meticulous data analysis and processing in wearable device applications and the high accuracy of the proposed model.

Biographie:
Fernando J. Velez (M93SM05) received the Licenciado, M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Instituto Superior Técnico, Technical University of Lisbon in 1993, 1996 and 2001, respectively. Since 1995 he has been with the Department of Electromechanical Engineering of Universidade da Beira Interior, Covilhã, Portugal, where he is Associate Professor.
He is also a senior researcher of Instituto de Telecomunicações. Fernando was an IEF Marie Curie Research Fellow in King’s College London (KCL) in 2008/09 (OPTIMOBILE IEF) and a Marie Curie ERG fellow at Universidade da Beira Interior from 2010 until March 2013 (PLANOPTI ERG). Fernando was the coordinator of the Instituto de Telecomunicações team in the Marie Skłodowska-Curie ITN Action (TeamUp5G) that started in 2019. He made or makes part of the teams of several European and Portuguese research projects on mobile communications, and he was the coordinator of six Portuguese projects. In 2018/19 was the coordinator of CONQUEST (CMU/ECE/0030/2017), an exploratory project from Carnegie Mellon University (CMU) Portugal, a collaboration with the Department of Engineering and Public Policy from CMU. Fernando was also co-chairing the Vertical Team 4 (Smart Buildings and Cities) within COST Action CA20120 INTERACT until 2024. Currently, he is coordinating the TALENT starting grant within the UNITA Alliance. Since 2026, Fernando became the elected IEEE ComSoc Portugal Chapter co-chair. From 2014 until 2020, Fernando Velez was proud of coordinating the Covilhã branch of the Instituto de Telecomunicações, a site of his research unit with around 25 Ph.D. researchers, more than 20 Ph.D. students and tens of M.Sc. students. From 2019 until now he has been one of the scientific coordinators of the prestigious Carnegie Melon University - Portugal research program. He has authored four books, 25 book chapters, 190 papers and communications in international journals and conferences, plus 49 in national conferences. His main research areas are cellular planning tools, traffic from mobility, wireless body sensor networks and wearable technologies, ML applied to radio resource management, spectrum management, and cost/revenue performance of advanced mobile communication systems.