08-10-2016, 12:51 PM
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The development of Wearable Health Monitoring Systems (WHMS) has been motivated mainly by increasing healthcare costs and by the fact that the world population is ageing. In addition to that, R&D 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. Furthermore,
due to embedded transmission modules and processing capabilities wearable health monitoring systems can facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring.
The majority of the currently developed WHMS research prototypes and products provide the basic functionality of continuously logging physiological data and possibly also that of alarm generation in case the sensed data exceed a predefined threshold value. In an effort to advance the capabilities of WHMS a step further, we are aiming at establishing a novel interactive, individualized
and intelligent wearable health monitoring prototype which we call Prognosis.
a) The design of 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,
b) the investigation of efficient embedded methods for detection of heart abnormalities in real-time ambulatory ECG recordings
c) the development of a system model based on Stochastic/Fuzzy Petri Nets and a corresponding simulation framework,
d) the incorporation of a system-patient dialogue interaction in order to capture non-measurable patient symptoms such as chest pain, dizziness, malaise etc,
e) the implementation of a user-adaptive learning strategy in order to enable the system to learn the healthy history of the user and thus to be able to derive patient specific decisions and finally
f) the setting up of a system prototype based on commercially available off-the-shelf components, e.g.
a smart-phone and Bluetooth-enabled bio-sensors.