Development of Wearable Technologies and Biosignal Processing Methods for the Assessment of Physiological States

In recent years, scientific breakthroughs in the biomedical field have spurred a growing interest in healthcare, driving research and development of increasingly sophisticated biomedical devices and data analysis algorithms capable of providing crucial information for the assessment of individual well-being. Among these, Wearable Health Devices (WHDs) stand out as some of the most sought-after technologies, thanks to their capability of being comfortably worn by the user and of allowing non-invasive and comfortable monitoring of the subject's health state through the acquisition of key physiological parameters. The user-friendly design, reduced weight and size, and the escalating performance in terms of energy efficiency and measurement accuracy incentivize the massive research and development of WHDs, suggesting their application as biomedical devices for monitoring not only vulnerable but also healthy individuals. These features pave the way to the prospective use of WHDs as helpful tools in the clinical setting for early diagnosis, potentially averting the onset of specific pathologies and helping to alleviate the workload of the healthcare system.Similarly, recent developments in the biomedical and clinical fields have involved the implementation and utilization of advanced data analysis algorithms that, starting from the raw signals acquired by biomedical devices, perform the extraction of important physiological indices for a more accurate assessment of organ functionality and individual physiological state. In fact, the growing interest from the scientific community in the field of biomedical signal processing has allowed researchers to identify new methodologies for the analysis and interpretation of biomedical data. This has brought to light innovative metrics, such as those derived from the information-theoretic domain and multivariate biomedical analysis, enabling the extraction of additional physiological indices to obtain novel insights into the dynamics of organ functions and their mutual influence in physiological and pathological states, thus allowing for a more comprehensive picture of physiological mechanisms and clinically relevant states. It appears evident how the combined and appropriate use of WHDs and algorithms for biomedical signal processing and analysis can be of fundamental importance in the healthcare context. For this reason, there is a need to continue researching and developing increasingly cutting-edge wearable technologies that enable multiparametric acquisition and the implementation of data analysis algorithms for the extraction and interpretation of standard and advanced physiological indices.This thesis addresses the concepts outlined above by (i) introducing a novel wearable biomedical device which has been specifically designed and realized to perform multiparametric and non-invasive acquisition of multiple biosignals detected in the same body area, and (ii) exploring both novel and standard techniques for biomedical data analysis to extract physiological indices capable of detecting diverse physiological states. Specifically, the realized device has been designed to be worn on the forefinger of the hand, it is user-friendly and allows for the acquisition of electrocardiographic (ECG), photoplethysmographic (PPG), skin conductance (SC), and motion signals. The device incorporates sensors for the non-invasive and comfortable acquisition, transmitting the data via a Bluetooth Low Energy (BLE) communication protocol to a computer equipped with a user interface specifically developed for the communication with the device, as well as for the real-time visualization and storage of the acquired data. The ability to easily and efficiently extract important physiological indexes simultaneously from the ECG and PPG, along with the acquisition of the SC signal, allow to employ the device for detecting physiological states, identifying conditions of physical and mental stress, and assessing cardiovascular system functionality, also enabling an investigation into the potential of the realized wearable device as diagnostic and monitoring tool in everyday life. It is for this reason that the thesis also addresses biomedical signal processing and analysis, introducing and investigating about post-processing and filtering operations on the acquired biosignals that lead to the extraction of different physiological parameters, such as respiratory rate, as well as the computation of physiological indices obtained from the time series of interest, such as RRI and PPI series (i.e., the time periods between successive heartbeats), that are obtained respectively from ECG and PPG signals. The thesis then explores Heart Rate Variability (HRV) analysis, which is a valid and widely used tool for the assessment of stress states, for extracting physiological indices in the time, frequency, and novel information-theoretic domains, over standard 5-minute time windows, performing what is well-known in the literature as Short-Term (ST) analysis. Furthermore, the work evaluates the feasibility of Ultra-Short-Term (UST) analysis, exploring an area which is currently under research and, due to the lower constraints associated with analyzing a very short time window, could represent a breakthrough in implementing this kind of analysis directly on wearable devices.Finally, the developed wearable device was employed in a measurement campaign aimed at preliminary validation of its use for detecting physiological states. This was achieved through the implementation of the discussed data analyses conducted using the recordings of biosignals acquired by the device.Data analysis algorithms are employed on the signals acquired by the realized wearable device, as well as other biomedical devices that has been used to collect biosignals, to evaluate the potential use of innovative indices, such as those obtained in the frequency and the information theoretical domain. Indeed, not all the analyses presented here were conducted on signals recorded with the wearable device, but the results of the analyses still serve to identify indices to be implemented on the wearable device in future developments of the present thesis work. In the first chapter, the main topic is introduced, presenting the potential of wearable biomedical devices along with the importance of developing non-invasive measurement methodologies and biomedical data analysis. These cutting-edge technologies aim to make healthcare accessible to everyone, effectively enhancing health awareness in civil, industrial, and clinical domains. Chapter 2 provides the background on which this work is based, introducing the relevant biosignals and subsequently presenting the methods that illustrate the types of data analysis employed. Chapter 3 covers the design and development of the ring-shaped device from the hardware, firmware, and software perspectives. Finally, Chapter 4 reports the results of the data analysis conducted on the processed biosignals, as well as the measurement campaign using the ring-shaped device to preliminarily validate its use for detecting physiological states.