Adaptive Multi Model Architecture by Using Similarity Between Trained User and New User Affective computing is a field of great interest in many applications, including video surveillance, behavior analysis, human robot interaction, lifelogging. While accurately predicting these technologies (e.g. emotion estimation, confident for talking a topic) could have benefits for many fields, generic machine learning systems still yield low performance in some situation. We hypothesize that these sophisticated models suffer from individual differences of humanxe2x80x99s personality. Therefore, we proposed a multi characteristic model architecture which combine the personalized machine learning models and utilize each model xe2x80x99s prediction score in the inference. This architecture formed with reference to ensemble machine learning architecture. In this research, we presented the calculation method of weighted average in multi characteristic architecture by using similarity between a new sample and trained characteristics. We estimated confidence during communication as a human internal state. Empirical results demonstrate that using multi model trained each person xe2x80x99s information to account for individual differences provides improvements over traditional machine learning system and insight for dealing with various individual types.

Using an Affective Computing Taxonomy Management System to Support Data Management in Personality Traits Affective Computing is a rather new and multidisciplinary research field that seeks sophisticated automation in emotion detection for later analysis. However, the automated emotion detection and analysis require as well comprehensive data management support, e.g. to keep control of data produced, and to enable its efficient reuse through classification with established terminology. This paper contributes to data management aspects in Affective Computing and to automation support in emotion classification on the basis of a personal traits analysis. Hence, we describe the implementation of a taxonomy management system, derived from requirements of a case study that investigates the relationship between personality and emotions in Affective Computing. The study makes use of machine learning software developed by SenseCare, an EUfunded Ru0026D project that applies Affective Computing to enhance and advance future healthcare processes and systems.