Improvement of Data Fusion with Threading Technology in Home UbiHealth

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. However, the fusion manipulations of large data volumes within strict time limits impose significant computational overhead. In the present work, threading technology is employed to take advantage of the processing capabilities of modern computers for the data fusion of the contextual parameter sensor readings, along with the selection of appropriate computing architectures and matching algorithms. The advantages offered by the proposed approach are presented and analyzed.