1,763 research outputs found

    Dance-the-music : an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

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    In this article, a computational platform is presented, entitled “Dance-the-Music”, that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers’ models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method can determine the quality of a student’s performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures

    Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance

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    Dance as a complex expressive form of motion is able to convey emotion, meaning and social idiosyncrasies that opens channels for non-verbal communication, and promotes rich cross-modal interactions with music and the environment. As such, realistic dancing characters may incorporate crossmodal information and variability of the dance forms through compact representations that may describe the movement structure in terms of its spatial and temporal organization. In this paper, we propose a novel method for synthesizing beatsynchronous dancing motions based on a compact topological model of dance styles, previously captured with a motion capture system. The model was based on the Topological Gesture Analysis (TGA) which conveys a discrete three-dimensional point-cloud representation of the dance, by describing the spatiotemporal variability of its gestural trajectories into uniform spherical distributions, according to classes of the musical meter. The methodology for synthesizing the modeled dance traces back the topological representations, constrained with definable metrical and spatial parameters, into complete dance instances whose variability is controlled by stochastic processes that considers both TGA distributions and the kinematic constraints of the body morphology. In order to assess the relevance and flexibility of each parameter into feasibly reproducing the style of the captured dance, we correlated both captured and synthesized trajectories of samba dancing sequences in relation to the level of compression of the used model, and report on a subjective evaluation over a set of six tests. The achieved results validated our approach, suggesting that a periodic dancing style, and its musical synchrony, can be feasibly reproduced from a suitably parametrized discrete spatiotemporal representation of the gestural motion trajectories, with a notable degree of compression

    Semantic Segmentation of Motion Capture Using Laban Movement Analysis

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    Many applications that utilize motion capture data require small, discrete, semantic segments of data, but most motion capture collection processes produce long sequences of data. The smaller segments are often created from the longer sequences manually. This segmentation process is very laborious and time consuming. This paper presents an automatic motion capture segmentation method based on movement qualities derived from Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and low-level kinematic features which, often yield unsophisticated segmentations. The LMA features are computed using a collection of neural networks trained with temporal variance in order to create a classifier that is more robust with regard to input boundaries. The actual segmentation points are derived through simple time series analysis of the LMA features

    Automated Analysis of Synchronization in Human Full-body Expressive Movement

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    The research presented in this thesis is focused on the creation of computational models for the study of human full-body movement in order to investigate human behavior and non-verbal communication. In particular, the research concerns the analysis of synchronization of expressive movements and gestures. Synchronization can be computed both on a single user (intra-personal), e.g., to measure the degree of coordination between the joints\u2019 velocities of a dancer, and on multiple users (inter-personal), e.g., to detect the level of coordination between multiple users in a group. The thesis, through a set of experiments and results, contributes to the investigation of both intra-personal and inter-personal synchronization applied to support the study of movement expressivity, and improve the state-of-art of the available methods by presenting a new algorithm to perform the analysis of synchronization

    The revised NEUROGES–ELAN system: An objective and reliable interdisciplinary analysis tool for nonverbal behavior and gesture

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    As visual media spread to all domains of public and scientific life, nonverbal behavior is taking its place as an important form of communication alongside the written and spoken word. An objective and reliable method of analysis for hand movement behavior and gesture is therefore currently required in various scientific disciplines, including psychology, medicine, linguistics, anthropology, sociology, and computer science. However, no adequate common methodological standards have been developed thus far. Many behavioral gesture-coding systems lack objectivity and reliability, and automated methods that register specific movement parameters often fail to show validity with regard to psychological and social functions. To address these deficits, we have combined two methods, an elaborated behavioral coding system and an annotation tool for video and audio data. The NEUROGES–ELAN system is an effective and user-friendly research tool for the analysis of hand movement behavior, including gesture, self-touch, shifts, and actions. Since its first publication in 2009 in Behavior Research Methods, the tool has been used in interdisciplinary research projects to analyze a total of 467 individuals from different cultures, including subjects with mental disease and brain damage. Partly on the basis of new insights from these studies, the system has been revised methodologically and conceptually. The article presents the revised version of the system, including a detailed study of reliability. The improved reproducibility of the revised version makes NEUROGES–ELAN a suitable system for basic empirical research into the relation between hand movement behavior and gesture and cognitive, emotional, and interactive processes and for the development of automated movement behavior recognition methods
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