Wearable Health Monitoring Systems (WHMS) have drawn a lot of attention from the research community and the industry during the last decade. The development of such systems has been motivated mainly by increasing healthcare costs and by the fact that the world population is ageing. In addition to that, RandD in WHMS has been propelled by recent technological advances in miniature bio-sensing devices, smart textiles, microelectronics and wireless communications techniques.
These portable health systems can comprise various types of small physiological sensors, which enable continuous monitoring of a variety of human vital signs and other physiological parameters such as heart rate, respiration rate, body temperature, blood pressure, perspiration, oxygen saturation, electrocardiogram (ECG), body posture and activity etc. As a result, and also due to their embedded transmission modules and processing capabilities, wearable health monitoring systems can constitute low-cost and unobtrusive solutions for ubiquitous health, mental and activity status monitoring.
The majority of the currently developed WHMS research prototypes and products provide the basic functionality of continuously logging and transmitting physiological data. However, WHMS have the potential of achieving early detection and diagnosis of critical health changes that could enable prevention of health hazardous episodes. To do that, they should be able to learn individual user baselines and also employ advanced information processing algorithms and diagnostics in order to discover problems autonomously and detect alarming health trends, and consequently, inform medical professionals for further assistance.
In an effort to advance the capabilities of a wearable system towards these goals, we focus in this dissertation on the development of a novel WHMS, called Prognosis. The developed prototype platform includes the following innovative features, which constitute the main research contributions of this work: a) a novel and highly accurate methodology for classifying ECG recordings on a resource constrained device which is based on the Matching Pursuits algorithm and a Neural Network, b) a physiological data fusion scheme based on a fuzzy regular formal language model, whereby the current state of the corresponding fuzzy Finite State Machine signifies the current health state and context of the patient, c) the extension of the decision making methodology based on a modified Fuzzy Petri Net (FPN) model, d) the integration of a user-learning strategy based on a neural-fuzzy extension of the FPN, e) the incorporation of a system-patient dialogue interaction in order to capture non-measurable patient symptoms such as chest pain, dizziness, malaise etc and finally f) the prototyping of the system based on a smart-phone that runs multi-threaded J2ME software for handling multiple simultaneous Bluetooth connections with off-the-shelf wireless bio-sensors.