Intelligent Smart Wrist Band-based Health Monitoring of Car Drivers - Baiense, João Pedro da Silva (2024)
Road 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.