Abstract
The range of ubiquitous computing technology available for use in healthcare continues to evolve, allowing for an increasing variety of wireless sensors, devices, and actuators to be deployed in changing environments. This paper presents a robust distributed architecture for adaptive and intelligent bio-interaction systems, called Evolutionary Bio-inspired Knowledge Accumulation. This system is designed to its capability to increase knowledge enhancement even in dynamic and uneven environments. Our proposed system adopts the concepts of biological context-awareness with evolutionary computations where the working environments are modeled and identified as bio-environmental contexts. We have used an unsupervised learning algorithm for bio-context modeling, and a supervised learning algorithm for context identification. A genetic algorithm, for its adaptive criteria, is used to explore action configuration for each identified bio context to implement our concept. This framework has been used to reduce noise in ECG signals that have been gathered in routine remote healthcare monitoring. Experimental results showed that the proposed algorithm effectively removes baseline wander noise and muscle noise, and feature extraction results showed a significant improvement of T duration extraction values.
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This work was supported by the R&D Program of MKE/KEIT.
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Kang, SK., Kim, JH., Chung, KY., Ryu, JK., Rim, KW., Lee, JH. (2013). Evolutionary Bio-Interaction Knowledge Accumulation for Smart Healthcare. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_52
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DOI: https://doi.org/10.1007/978-94-007-5860-5_52
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