VALE-Emotions: Teaching mobile application for individuals with Autism Spectrum Disorders

In this paper, the development of an interactive mobile application to strengthen the learning of emotion recognition in children with Autism Spectrum Disorders (ASD) is presented. This App is part of an authoring Virtual Advanced Learning Environment (VALE) devoted to support teaching and learning activities. VALE-Emotions App is based on the six basic emotions studied by (Paul Ekman, 1992), but it is not only limited to the recognition of such emotions in their highest intensity levels. In fact, the app allows for determining the effective recognition of these emotions at different intensity levels. Such intensity levels are generated by an authoring Dynamic Facial Expressions (DFE) coding using virtual avatars. Each learning activity is carried out through training and tests applications, giving to the users the opportunity of freely developing, learning, and strengthen social skills in an entertaining way. The results of the experimentation of the VALE-Emotions on subjects with ASD are also reported. In general, the participants showed efficient response at the stimulus during the developed activities obtaining a high and fast recognition of certain emotions

Motion Level-of-Detail: A Simplification Method on Crowd Scene

Recent technological improvement in character animation has increased the number of characters that can appear in a virtual scene. Besides, skeletal and mesh structures are expected to be more complex in the future. Therefore, simulating massive characters' joints in a real-time crowd environment without any preprocessing is unaffordable. We propose a preprocessing method called 'motion level-of-detail' to overcome this limitation. Our 'motion level-of-detail' framework not only minimizes the simulation cost of the joints, but also maintains the similarity between the original and the simplified motion. 'Joint posture clustering (JPC)', which is the skeletal simplification method of our framework, reduces skeletal node by the clusters of similar postures. A cluster is a set of continuous frames, where each frame has similar posture. Because our approach depends on motion trajectory, simplified result preserves the quality of the motion. We also applied a geometric simplification on deformable character mesh, to increase performance. Our approach was particularly useful for the complex skeletal motions that have a monotonous trajectory