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- Wearable solution for health monitoring of car driversPublication . Baiense, João Pedro; Coelho, Paulo Jorge; Pires, Ivan Miguel; Velez, Fernando J.The need for creative solutions in real-time health monitoring has been highlighted by the rise in health-related incidents involving drivers of motor vehicles. It has led to the development of wearable technology that seamlessly integrates with the Internet of Medical Things (IoMT) to improve driver safety and healthcare responsiveness. The development of a revolutionary wearable technology system is presented in this study as an innovative approach to vehicle safety and healthcare. This system's real-time ability to track a driver's health is a significant development in guaranteeing driver safety and wellness. The study examines the hardware component's complex design and implementation, particularly concerning the printed circuit board (PCB) layout and electrical schematic. The gadget emphasizes wearability, robustness, affordability, and user-friendliness and is a shining example of valuable and effective medical technology. The research delves deeper into possible improvements for the system, like adding complex algorithms and a user-friendly interface. Enhancing user involvement and system intelligence hopes to maximize the system's potential for real-time health monitoring. The significance of this study in utilizing Internet of Medical Things (IoMT) technology is highlighted by its junction with multiple fields, including electronics, hardware engineering, human-computer interaction, and health informatics. This dissertation emphasizes the potential of wearable technology in bridging the gap between healthcare monitoring and vehicle safety by focusing on real-time health monitoring in the automotive context
- Intelligent PPG-based Heart Rate Signal Analysis for Car Drivers MonitoringPublication . Baiense, João Pedro; Eerdekens, Anniek; Schampheleer, Jorn; Deruyck, Margot; Pires, Ivan Miguel; Velez, Fernando JoséThis research aims to contribute to enhancing road safety through the development and exploration of an intelligent wristbandbased 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 eticulous data analysis and processing in wearable device applications and the high accuracy of the proposed model.