A Health Prognosis Wearable System with Learning Capabilities Using NNs
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
A survey on wearable sensor-based systems for health monitoring and prognosis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Prognosis: a wearable health-monitoring system for people at risk: methodology and modeling
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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In this paper we present our efforts towards establishing a wearable platform that utilizes off-the-shelf Bluetooth-enabled sensors in order to preprocess and analyze streaming physiological recordings. A smart-phone running multi-threaded J2ME software is utilized for handling multiple simultaneous Bluetooth connections and a network socket connection with a remote workstation. Received measurements of signals such as the ECG and PPG are decomposed through appropriately selected Wavelet Transforms with the purpose of identifying unusable segments that have been severely corrupted by noise and de-noising the remaining usable data portions. We study the use of the undecimated wavelet packet transform for ECG noise removal. Provided results illustrate the advantages of the proposed decomposition for wavelet denoising compared to conventional approaches, at the cost however of performing more computations. The described wearable platform along with the documented data preprocessing steps is employed as the front end of a closed-loop intelligent and interactive system termed Prognosis. This system is capable of facilitating ubiquitous and unsupervised round-the-clock health monitoring of people at risk, as it is able to a) address the issue of unsupervised data collection and b) to interact with the patient via an automated speech-dialogue system.