A Fuzzy Inference Algorithm for Personal Bio-Informatics Fusion in Home-care System

Abstract

In the real world, people may not always clearly understand what the other people mean when they communicate. This holds true as well whenever changes occur in their individual health conditions. The same situation is present in the judgment of multiple bio-signals because a similarly fuzzy situation exists. If personal bio-signals are collected, we can judge that a bio-signal overtakes its thresholds. In this research, we applied the fuzzy theory to fuse multiple bio-signals. In addition, we were able to identify the weak patients in a group. In this study, a fuzzy inference algorithm for fusing multiple bio-signals and referring to a person 􀀀 s health was proposed, as a result of which, the weaker persons in the same group were easily identified. Moreover, the inference results were outputted by semantic representations such as high, median, or low. The boundary values of degrees were also defined through special test cases. Furthermore, the degree range was divided into parts to differentiate between the healthy levels of patients.