Communication system for a tooth-mounted RF sensor used for continuous monitoring of nutrient intake

In this Thesis, the communication system of a wearable device that monitors the user’s diet is studied. Based in a novel RF metamaterial-based mouth sensor, different decisions have to be made concerning the system’s technologies, such as the power source options for the device, the wireless technology used for communications and the method to obtain data from the sensor. These issues, along with other safety rules and regulations, are reviewed, as the first stage of development of the Food-Intake Monitoring projectOutgoin

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

TriSense: RFID, radar, and USRP-based hybrid sensing system for enhanced sensing and monitoring

This thesis presents a comprehensive approach to contactless human activity recognition (HAR) using the capabilities of three distinct technologies: radio frequency identification (RFID), Radar, and universal software-defined radio peripheral (USRP) for capturing and processing Wi-Fi-based signals. These technologies are then fused to enhance smart healthcare systems. The study initially utilises USRP devices to analyse Wi-Fi channel state information (CSI), choosing this over received signal strength for more accurate activity recognition. It employs a combination of machine learning and a hybrid of deep learning algorithms, such as the super learner and LSTM-CNN, for precise activity localisation. Subsequently, the study progresses to incorporate a transparent RFID tag wall (TRT-Wall) that employs a passive ultra-high frequency (UHF) RFID tag array. This RFID system has proven highly accurate in distinguishing between various activities, including sitting, standing, leaning, falling, and walking in two directions. Its effectiveness and non-intrusiveness make it particularly suited for elderly care, achieved using a modified version of the Transformer model without the use of a decoder. Furthermore, a significant advancement within this study is the creation of a novel fusion (RFiDARFusion) system, which combines RFID and Radar technologies. This system employs a long short-term memory networks variational autoencoder (LSTM-VAE) fusion model, utilising RFID amplitude and Radar RSSI data. This fusion approach significantly improves accuracy in challenging scenarios, such as those involving long-range and non-line-of-sight conditions. The RFiDARFusion system notably improves the detection of complex activities, highlighting its potential to reduce healthcare costs and enhance the quality of life for elderly patients in assisted living facilities. Overall, this thesis highlights the significant potential of radio frequency technologies with artif icial intelligence, along with their combined application, to develop robust, privacy-conscious, and cost-effective solutions for healthcare and assisted living monitoring systems

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy