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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.
- Intelligent Smart Wrist Band-based Health Monitoring of Car DriversPublication . Baiense, João Pedro da Silva; Velez, Fernando José da Silva; Pires, Ivan Miguel SerranoRoad accidents are often related to drivers’ psychological state and are frequently overlooked and dismissed. This includes the drivers’ mental health, which can be negatively affected by conditions such as stress, fatigue, and sadness. Emotional disturbances can impair driving abilities, posing significant risks to drivers and road users. Public health initiatives must focus on integrating innovative solutions to minimize fatalities and injuries. The Internet of Medical Things (IoMT) is a field of research that focuses on developing cost-effective nonintrusive new methods for assessing vital signs in non-clinical settings, such as homes and vehicles. The increasing use of these applications underscores the potential to address healthrelated road risks. Given the pressing concerns of road safety, this dissertation proposes the creation of an IoMT system that can revolutionize the driving experience. By introducing real-time monitoring of driver health, this system aims to address these challenges and significantly enhance road safety, a crucial need in our society. A systematic review, including the choice of thirtytwo relevant scientific publications on wearable devices for healthcare monitoring, was conducted to create a reliable system. The review utilized Natural Language Processing and the PRISMA methodology to analyze papers from various databases and considered population, methods, sensors, features, and communication protocols. The studies highlighted various hardware and software technologies used to enhance healthcare monitoring applications and the benefits and challenges associated with these applications, providing an overview of how to build an efficient system. Based on the results of the systematic review, the Driver Health System was proposed, integrating multiple layers with distinct roles to ensure efficiency and high performance. This dissertation proposes an innovative device for measuring the driver’s health data, integrating a comprehensive set of sensors and power management components to ensure reliable functionality. The device’s printed body encapsulates the PCB and battery, optimizing functionality and user comfort. The firmware developed for the device presented in this dissertation showcases the sensor drivers for photoplethysmography (PPG), accelerometer, barometric pressure, and fuel gauge sensors. The dissertation proposes a deep learning model designed to estimate the user’s heart rate by leveraging data from the PPG and accelerometer sensors. The model development involves multiple processing steps. Leaveone-session-out cross-validation and hyperparameter tuning techniques were employed for the model training and evaluation. The model achieved an outstanding Mean Absolute Error (MAE) of 3.450 ± 1.324 bpm and a Mean Squared Error (MSE) of 69.50 ± 93.57 bpm2 . The model was deployed in a custom WEB application for testing purposes. The dissertation describes the development of a custom mobile application for the Driver Health System, which offers crucial features such as intuitive real-time access to health status, device compatibility, power management, and integration of the heart rate estimation model to provide users with deeper insights into their health condition. This dissertation successfully enables a robust, innovative, real-time driver health monitoring solution. The Driver Health System represents a significant advancement at the intersection of healthcare industry and automotive sector. It aims to enhance road safety and establish a connected network that empowers to monitor and manage the drivers’ health effectively.