Fused Contextual Data With Threading Technology to Accelerate Processing in Home UbiHealth

Fused Contextual Data With Threading Technology to Accelerate Processing in Home UbiHealth

John Sarivougioukas, Aristides Vagelatos
Copyright: © 2022 |Pages: 14
DOI: 10.4018/IJSSCI.285590
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Abstract

According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents’ health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. The employment of threading technology accelerates the computational process and carrying out benchmarks on publicly available data set, is shown to be more efficient. Thus, threading technology proved promising for home UbiHealth applications by lowering the number of required cooperating computers.
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Background Information

In the Home UbiHealth environment, dispersed and loosely coupled devices capture the occurring events and contribute to the development of context, which is used for medical inference. The developed context is affected by various sources of influence such as hardware failures, energy limitations, noise, and frequently interleaving devices. In this framework, Bayesian networks and hidden Markov chains are commonly used to calculate occurrence probabilities of events.

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