This paper proposes a fusion model that merges the context-aware multimodal information of JADE (Java Agent DEvelopment Framework). The context-aware multimodal information system is developed from the multi-heterogeneous context sensing devices. This multimodal not only gathers multidimensional data that aims to recognize and analyze the collected emotion information, but also emotion manages the context-aware information. According to the collections of users use remote control usages during watching TV and the face recognition technology, we developed a context-aware multimodal information system to recognize emotions. The emotion information is reasoned from the action data of remote control usages that combines with the emotion information gathered from the face recognition. These two information fuses with the feedback mechanism of real emotion to acquire the information of personal emotion representation. This fusion model of context-aware multimodal information provides personal emotion information and learning mechanism to reason the information from contextaware ubiquitous environment applied on personal emotion prediction.

In recent years, personal health management has been interested to researchers and healthcare practitioners. Recording and analyzing physiological variations in ordinary life could be especially useful to manage health problems and to care individuals. It is widely pointed out that various vital signs are important indicators used to evaluate the wellness of physical bodies. In this study, an Intelligent-Mamdani Inference Scheme (IMIS) based on fuzzy markup language (FML) is proposed to apply to the semantic decision-making for personal health in healthcare applications. The IMIS could provide semantic analysis of personal health status by using the knowledge base and fuzzy inference rules, which are preestablished by domain experts. This scheme is a well-defined composition, including a FML editor, a FML parser, a fuzzy inference mechanism and a semantic decision-making mechanism. The experimental results show that the proposed scheme is feasible for semantic decision of personal health. A person can understand his physical conditions via the generated semantic decision-marking mechanism by the input of vital signs.

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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.

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