Far beyond very fine-grained, accurate data, movement-based interaction design often benefits from working with higher-level features to create engaging interactions. Yet, movement-based frameworks most often focus on the detection strategy without providing clarity on how to use such data for the design of user interactions. This purely technical approach makes it challenging for practitioners to explore feature extraction technology as a design material. This paper presents an approach that considers designers’ perspectives and attitudes about using movement-based features to propose a set of designer-interpretable descriptors and enhance their ability to use human motion in interaction design. In addition, a computational prototype is utilized to visually present the features and help designers better understand movement content. The investigation into the effects of designer-interpretable features on the concept ideation and design of motion-based interactions followed a user-centered approach, and its validity was established through a qualitative study conducted with interaction designers. Semi-structured interviews following a creative practice exercise were employed to evaluate how the computational prototype influenced interaction designers’ processes, allowing them to reflect on their experience and compare it to previous approaches when designing movement-based interactions. The study determined that interaction designers were able to leverage features to identify detection capabilities and enrich the ideation process. Moreover, feature visualization provided further insights into the characteristics of movement, which helped practitioners understand the interaction opportunities that come with it. • Designer-interpretable features enable non-technical practitioners to use motion analysis. • Spatial visualizations make group detection accessible without algorithmic expertise. • Five themes reveal how accessible features transform installation design practice. • Designers prioritize spatial patterns over biomechanical precision for public spaces. • Evidence-based iteration replaces technology-constrained design in motion systems.

Movement-based full-body interactions are increasingly being used in the design of interactive spaces, computer-mediated environments, and virtual user experiences due to the development and availability of diverse sensing technologies. In this context, the role of interaction designers is to find systematic and predictable relationships between bodily actions and the corresponding responses from technology. Sensor-based interaction design relies on sensor data analysis and higher-level feature extraction to improve detection capabilities. However, understanding human movement to inform the design of motion-based interactions is not straightforward if the detection capabilities of interaction technologies are unknown. We aim at understanding the problems and opportunities that practitioners—regardless of their technical background—perceive in using different motion-based full-body features. To achieve this, we conducted four separate focus groups with experienced practitioners, with and without technical backgrounds. We used a framework for the analysis of focus group data in information systems research to identify content areas and draw conclusions. Our findings suggest that most interaction designers, regardless of their technical background, consider motion-based feature extraction to be challenging and time-consuming. However, participants acknowledge they might use designer-interpretable features as a potential tool to foster user behavior exploration. Understanding how practitioners link sensor-based interaction design with feature extraction technology is relevant to design computational tools and reduce the technical effort required from designers to characterize the user’s movement.

This paper reflects on the effectiveness of current Learning Management Systems and proposes one whose information structure and interface are designed according to a specific educational model -one centered on learning activities. Likewise, the most significant results of evaluating the first version of the platform are presented.

Online discussion forums are a key element in virtual learning environments. The way learners participate in discussion boards can be a very useful source of indicators for teachers to facilitate their tasks. The use of a two-stage analysis strategy based on an agglomerative hierarchical clustering algorithm is proposed in this paper to identify different participation profiles adopted by learners in online discussion forums. Different parameters are used to characterize learners' activity (amount of posts, rhythm, depth of threads, crossed replies, etc). Participation profiles are identified and analyzed in terms of behavior and performance.

Due to the idiosyncrasy of online education, students may become disoriented, frustrated or confused if they do not receive the support, feedback or guidance needed to be successful. To avoid this, the role of teachers is essential. In this regard, instructors should be facilitators who guide students throughout the teaching-learning process and arrange meaningful learner-centered experiences. However, unlike face-to-face classes, teachers have difficulty in monitoring their learners in an online environment, since a lot of learning management systems provide faculty with student tracking data in a poor tabular format that is difficult to understand. In order to overcome this drawback, this paper presents a novel graphical educational monitoring tool based on faceted browsing that helps instructors to gain an insight into their classrooms' performance. Moreover, this tool depicts information of each individual student by using a data portrait. Thanks to this monitoring tool, teachers can, on the one hand, track their students during the teaching-learning process and, on the other, detect potential problems in time.

Cognitive overload, confusion and disorientation are common drawbacks in e-learning. To cope with these difficulties, this work suggests a course structure focused on the activity. This proposal also tries to help learners by improving guidance and interaction with the structure by means of information visualization techniques, which have proven to be useful in other domains. Students would carry out their learning process through a set of activities, which can be accessed from four information spaces (i.e. competences and objectives, scheduling, table of contents and assessment). Hence, this proposal should help students in their daily work.

The synthesis of talking heads has been a flourishing research area over the last few years. Since human beings have an uncanny ability to read people's faces, most related applications (e.g., advertising, video-teleconferencing) require absolutely realistic photometric and behavioral synthesis of faces. This paper proposes a person-specific facial synthesis framework that allows high realism and includes a novel way to control visual emphasis (e.g., level of exaggeration of visible articulatory movements of the vocal tract). There are three main contributions: a geodesic interpolation with visual unit selection, a parameterization of visual emphasis, and the design of minimum size corpora. Perceptual tests with human subjects reveal high realism properties, achieving similar perceptual scores as real samples. Furthermore, the visual emphasis level and two communication styles show a statistical interaction relationship.

This paper presents a new method named text to visual synthesis with appearance models (TEVISAM) for generating videorealistic talking heads. In a first step, the system learns a person-specific facial appearance model (PSFAM) automatically. PSFAM allows modeling all facial components (e.g. eyes, mouth, etc) independently and it will be used to animate the face from the input text dynamically. As reported by other researches, one of the key aspects in visual synthesis is the coarticulation effect. To solve such a problem, we introduce a new interpolation method in the high dimensional space of appearance allowing to create photorealistic and videorealistic avatars. In this work, preliminary experiments synthesizing virtual avatars from text are reported. Summarizing, in this paper we introduce three novelties: first, we make use of color PSFAM to animate virtual avatars; second, we introduce a nonlinear high dimensional interpolation to achieve videorealistic animations; finally, this method allows to generate new expressions modeling the different facial elements.

An unsupervised application for the visual analysis of metallic substrates and paint coatings is presented. Model based classification is used for the analysis of metallic substrates while a segmentation of the image is required to distinguish between paint and substrate or between paint and rust for the analysis of paint coatings. A unified framework based on the expectation-maximization (EM) algorithm is presented to solve both analyses. The EM algorithm is used both as a colour-based identification method for measuring cleanness properties of the substrate's surface, and also as a colour-based segmentation method for extracting the paint regions of the coating's surface. An initialisation method and a pre-processing step are proposed to avoid false segmentations. Experimental results on real samples show more repeatability and accuracy than the results obtained by human visual inspection. Each analysis has been visually illustrated with a representative example.

In this paper we present a speech synthesis method for diphonebased text-to-speech systems. Its main goal is to achieve\nprosodic modifications that result in more natural-sounding synthetic speech. This improvement is especially useful for emotional speech synthesis, which requires high-quality prosodic modification. We present a hybrid method based on TD-PSOLA and the harmonic plus noise model, which incorporates a novel method to jointly modify pitch and time-scale. Preliminary results show an improvement in the synthetic speech quality when high pitch modification is required.

This paper describes a 2D realistic talking face. The facial appearance model is constructed with a parameterised 2D sample based model. This representation supports moderated head movements, facial gestures and emotional expressions. Two main contributions for talking heads applications are proposed. First, the image of the lips is synthesized by means of shape and texture information. Secondly, a nearly automated training process makes the talking face personalization easier, due to the use of mouth tracking. Additionally, lips are synchronized in real time with speech that is generated using a SAPI compliant text-to-speech engine.

Traditional techniques for tracking nonrigid objects such as optical flow, correlation, active contours or color, cannot deal with situations where image changes are not due to motion but appearance (e.g. tracking the lips when the teeth appear). Two main contributions for appearance tracking of flexible objects are proposed. The first one is a flexible generalization of eigentracking within the same robust continuous optimization framework. The second one is a generalization of traditional graylevel eigenspaces, constructing a multiple channel "eigenspace" using filter responses to give robustness against variations in the training conditions such as illumination changes. Additionally, 3D geometric transformations are incorporated, a regularization term is added for numerical stability reasons and the optimization problem is solved in closed form. Experiments on lip tracking are reported.