Interaction between humans and humanoid avatar representations is very important in virtual reality and robotics, since the humanoid avatar can represent either a human or a robot in a virtual environment. Many researchers have focused on providing natural interactions for humanoid avatars or even for robots with the use of camera tracking, gloves, giving them the ability to speak, brain interfaces and other devices. This paper provides a new multimodal interaction control for avatars by combining brain signals, facial muscle tension recognition and glove tracking to change the facial expression of humanoid avatars according to the user's emotional condition. The signals from brain activity and muscle movements are used as the emotional stimulator, while the glove acts as emotion intensity control for the avatar. This multimodal interface can determine when the humanoid avatar needs to change their facial expression or their walking power. The results show that humanoid avatar have different timelines of walking and facial expressions when the user stimulates them with different emotions. This finding is believed to provide new knowledge on controlling robots' and humanoid avatars' facial expressions and walking.
The implementations of data integration in current days have many issues to be solved. Heterogeneity of data with non-standardization data, data conflicts between various data sources, data with a different representation, as well as semantic aspects problems are among the challenges and still open to research. Semantic data integration using ontology approach is considered as an appropriate solution to deal with semantic aspects problem in data integration. However, most methodologies for ontology development are developed to cover specific purpose and less suitable for common data integration implementation. This research offers an improved methodology for ontology development on data integration to deal with semantic aspects problem, called OntoDI. It is a continuation and improvement of the previous work about ontology development methods on agent system. OntoDI consists of three main parts, namely the pre-development, core-development and postdevelopment, in which every part contains several phases. This paper describes the experiment of OntoDI in the electronic learning system domain. Using OntoDI, the development of ontology knowledge gives simpler phases, complete steps, and clear documentation for the ontology client. In addition, this ontology knowledge is also capable to overcome semantic aspect issues that happen in the sharing and integration process in education area.
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