Fuzzy intelligent inference scheme for self-health estimation via blood pressure and body mass index in out-of-hospital

Abstract

E-health for both chronic patients and wellness persons has recently attracted the interest of researchers and practitioners. The physical vital signs are one of the most important factors used to evaluate individual wellness. The variations of daily vital signs are even significant in analyzing physical health trends that can be applied in self-caring for individuals at home. In this paper, an Intelligent-Mamdani Inference Scheme (IMIS) based on fuzzy markup language (FML) is proposed to define approximate health conditions of the individuals via the blood pressure and the body mass index in out-of-hospital. The IMIS can fuse these vital signs and infer semantic health summary using the constructed fuzzy rules and the knowledge base. The main contributions of the this paper are: (1) to leverage personal vital signs by fuzzy logic technology for self-health management at home; (2) to design the novel scheme in detail for practicability and reproduction. The experimental results show that the scheme is feasible to infer personal health status. An individual can easily recognize self-health trend through semantic sentence generation, which further advances self-health management in out-of-hospital.