Transforming Intangible Folkloric Performing Arts into Tangible Choreographic Digital Objects: The Terpsichore Approach

Doulamis, Anastasios; Voulodimos, Athanasios; Doulamis, Nikolaos; Soile, Sofia; Lampropoulos, Anastasios

doi:10.5220/0006347304510460

Cited by 45 publications

(25 citation statements)

References 23 publications

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“…A number of methods have been proposed for motion comparison, indexing, retrieval, and summarization that explicitly deal with dancing, e.g., for modern [4,32], and folk dance [8,30,80], resulting in numerous dance-oriented applications, e.g., dance synthesis, teaching, games, annotation, and the like [101]. For instance, Shiratori et al [89] synthesize dance movements to match given music, Fukayama and Goto [41] use machine learning to generate music-driven dance movements, while Aristidou et al [9] build, in the context of Motion Graphs [62], an LMA-derived motion analysis framework that eliminates potentially problematic transitions and synthesizes style-coherent dance motions.…”

Section: Dance and Intangible Cultural Heritagementioning

confidence: 99%

Digital Dance Ethnography

Aristidou

Shamir

Chrysanthou

2019

J. Comput. Cult. Herit.

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Folk dances often reflect the socio-cultural influences prevailing in different periods and nations; each dance produces a meaning, a story with the help of music, costumes and dance moves. However, dances have no borders; they have been transmitted from generation to generation, along different countries, mainly due to movements of people carrying and disseminating their civilization. Studying the contextual correlation of dances along neighboring countries, unveils the evolution of this unique intangible heritage in time, and helps in understanding potential cultural similarities. In this work we present a method for contextually motion analysis that organizes dance data semantically, to form the first digital dance ethnography. Firstly, we break dance motion sequences into some narrow temporal overlapping feature descriptors, named motion and style words, and then cluster them in a high-dimensional features space to define motifs. The distribution of those motion and style motifs creates motion and style signatures, in the content of a bag-of-motifs representation, that implies for a succinct but descriptive portrayal of motions sequences. Signatures are timescale and temporal-order invariant, capable of exploiting the contextual correlation between dances, and distinguishing fine-grained difference between semantically similar motions. We then use quartet-based analysis to organize dance data into a categorization tree, while inferred information from dance metadata descriptions are then used to set parent-child relationships. We illustrate a number of different organization trees, and portray the evolution of dances over time. The efficiency of our method is also demonstrated in retrieving contextually similar dances from a database.

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Section: Dance and Intangible Cultural Heritagementioning

confidence: 99%

Digital Dance Ethnography

Aristidou

Shamir

Chrysanthou

2019

J. Comput. Cult. Herit.

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“…Stavrakis [3] recorded the performances of expert dancers with high-quality motioncaptured data. In addition, some existing projects aiming at archiving and protecting intangible cultural heritages [4], [6], [22] also use Kinect and other multi-modal sensors to acquire the motion data of human dances.…”

Section: Related Work a Motion-captured Datamentioning

confidence: 99%

Dance Movement Learning for Labanotation Generation Based on Motion-Captured Data

Zhang

2019

IEEE Access

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Labanotation is a widely used notation system for recording body movements, especially dances. It has wide applications in choreography preservation, dance archiving, and so on. However, the manual creation of Labanotation scores is rather difficult and time-consuming. Therefore, research on the generation of Labanotation scores is of great interest. In this paper, we aim to generate Labanotation scores based on the motion-captured data obtained from real-world dance performances. First, to deal with challenges such as various dance movement patterns, different dancer shapes, and noises in the motion-captured data, we propose a novel feature that is invariant to anthropometric variation and body orientation. Then, we generate the notations of both lower-limb movements and upper-limb gestures. On the one hand, we utilize the hidden Markov model (HMM) to analyze the temporal dynamic characteristics of limb movements and map each lower limb movement to a corresponding dance notation. On the other hand, for upper limbs, we train a multi-class classifier based on the extremely randomized trees (Extra-Trees) to identify the notations for arm gestures. Finally, we generate the Labanotation symbols based on the above movement analysis and thus create Labanotation scores. The proposed methods can generate the spatial symbols describing directions and levels in both the support column and arm column based on motion-captured data. The generated scores are clear and reliable. Experimental results show an average recognizing accuracy of over 92% for the generated notations, which is significantly better than previous work. INDEX TERMS Labanotation, motion-captured data, hidden Markov models, extremely randomized trees, dance movement analysis.

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“…Furthermore, advances in the capture, storage and analysis of intangible heritage (e.g. Doulamis et al 2017;Aristidou et al 2017) are creating large bodies of rich and complex data which now requires effective means of presentation for serious games.…”

Section: Examples Of the Model In Serious Games For Intangible Heritagementioning

confidence: 99%

A model of heritage content to support the design and analysis of video games for history education

Hanes

Stone

2018

J. Comput. Educ.

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In recent years, increasing academic attention has been focused on the educational potential of video games. Serious games for heritage applications have received much interest, and many examples are well documented in the literature. Many frameworks and methodologies have been proposed for how to design serious games, yet there is a lack of guidance for how to embed heritage information in the mechanics and content of these games. There are also many examples of commercial (non-serious) games that contain heritage content, and there is interest in how these games can be used in educational contexts. However, these games are designed primarily for entertainment purposes, without pedagogical foundations and with limited historical accuracy. There is currently a lack of a rigorous, scientific approach to support the critical analysis of the content of these games and to inform their use within learning settings. In this paper, we propose that the above issues are related by a lack of definitions of heritage content in the video game medium. We describe a model that defines how historical information can be presented in a video game at a content level and demonstrate how it can be applied to the analysis of the content in a commercial historical game. Finally, we propose a novel methodology based on activity theory to guide the design of serious games, based on pre-defined heritage instructional content. The findings are relevant both to educators who wish to use heritage video games, and to designers of serious games for heritage.

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Transforming Intangible Folkloric Performing Arts into Tangible Choreographic Digital Objects: The Terpsichore Approach

Cited by 45 publications

References 23 publications

Digital Dance Ethnography

Digital Dance Ethnography

Dance Movement Learning for Labanotation Generation Based on Motion-Captured Data

A model of heritage content to support the design and analysis of video games for history education

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