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2013
Ochs, M.; Pelachaud, C. (2013): Socially Aware Virtual Characters: The Social Signal of Smiles [Social Sciences]. In: IEEE Signal Processing Magazine 30 (2), S. 128-132. DOI: 10.1109/MSP.2012.2230541

DOI: https://doi.org/10.1109/MSP.2012.2230541

Abstract: At first, machines were mainly used to solve complex mathematical problems. Today, they often embody more and more roles typically endowed by humans, such as a tutor in a virtual learning class or an assistant for virtual task realization. Moreover, the paradigm highlighted in [1] reveals that the user’s relation with the computer is intrinsically social, with high similarities compared to interpersonal relationships. For instance, people have no problem using communicative means (verbal and nonverbal behavior) to interact with artificial entities and apply polite rules. One particularly interesting aspect of the human machine interaction is the bidirectionality of the relation: users exhibit social behaviors to the computer, and the machine’s behavior affects users. Reeves and Nass [1] have shown that people tend to react naturally and socially to computers, as they would do to another person. In such a human-machine interaction, which can be seen as a particular social context with users sensitive to the computer behavior, the development of socially aware virtual characters appears fundamental.

Keywords: Context awareness, Heuristic algorithms, Human Factors, human machine interaction, ieee xplore, interpersonal relationship, Künstliche Intelligenz, Machine intelligence, Man machine systems, Nonverbal behavior, smile, Social Behavior, social sciences, social sciences computing, socially aware virtual character, user interfaces, user relation, Verbal Behavior, Virtual environments, virtual learning class, Virtual reality, virtual task realization
2012
Lin, Hao-Chiang Koong; Wang, Cheng-Hung; Chao, Ching-Ju; Chien, Ming-Kuan (2012): Employing Textual and Facial Emotion Recognition to Design an Affective Tutoring System. In: Turkish Online Journal of Educational Technology - TOJET 11 (4), S. 418-426. Online verfügbar unter https://eric.ed.gov/?id=EJ989317, zuletzt geprüft am 22.11.2019

Abstract: Emotional expression in Artificial Intelligence has gained lots of attention in recent years, people applied its affective computing not only in enhancing and realizing the interaction between computers and human, it also makes computer more humane. In this study, emotional expressions were applied into intelligent tutoring system, where learners’ emotional expression in learning process was observed in order to give an appropriate feedback. Emotional intelligent not only gives high flexibility to the interaction of tutoring system, it also to deepen its level of human interaction. This study uses dual-mode operation: facial expression recognition, and text semantics as the main elements in affective computing to understand users’ emotions. Text semantics are used to understand learners’ learning status, and the results would contribute to course management agents in order to choose the most appropriate teaching strategies and feedback to the users. Facial expression recognition allows interactive agents to provide users a complete sound and animation feedback. (Contains 5 tables and 3 figures.)

Keywords: Affective Behavior, Animation, Artificial intelligence, Bedienung & Handhabung, Computer Assisted Instruction, Computer Software Evaluation, Computer System Design, Educational Technology, Feedback (Response), Focus Groups, Foreign Countries, Grounded Theory, Intelligent tutoring systems, Man machine systems, Mixed Methods Research, Multimedia Instruction, Natural Language Processing, Nonverbal communication, Programming, Psychological Patterns, Rating Scales, Teaching Methods, usability, Use Studies
2007
Jaeckel, P.; Campbell, N.; Melhuish, C. (2007) : Towards Realistic Facial Behaviour in Humanoids - Mapping from Video Footage to a Robot Head: 2007 IEEE 10th International Conference on Rehabilitation Robotics: Piscataway NJ: IEEE, S. 833-840

DOI: https://doi.org/10.1109/ICORR.2007.4428521

Abstract: Rehabilitation robotics and physical therapy could greatly benefit from engaging and motivating, robotic caregivers which respond in accordance to patients’ emotional and social cues. Recent studies indicate that human-machine interactions are more believable and memorable when a physical entity is present, provided that the machine behaves in a realistic manner. It is desirable to adopt face-to-face communication because it is the most natural and efficient way of exchanging information and does not require users to alter their habits. Towards this end, we describe a process for animating a robot head, based on video input of a human head. We map from the 2D coordinates of feature points into the robot’s servo space using Partial Least Squares (PLS). Learning is done using a small set of keyframes manually created by an animator. The method is efficient, robust to tracking errors and independent of the scale of the face being tracked.

Keywords: Animation, Computer animation, Facial animation, human-machine interactions, Humanoid Robots, Humanoids, Humans, ieee xplore, Künstliche Intelligenz, Learning, learning (artificial intelligence), Magnetic heads, Man machine systems, medical robotics, Medical treatment, Orbital robotics, partial least squares, patient rehabilitation, physical therapy, realistic facial behaviour, Rehabilitation robotics, robot head, Robot kinematics, robot servo space, servomechanisms, video footage

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