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Salsa dance learning evaluation and motion analysis in gamied virtual reality environment

Simon Senecal; Niels A. Nijdam; Andreas Aristidou; Nadia Magnenat-Thalmann


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        <foaf:name>Simon Senecal</foaf:name>
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        <foaf:name>Andreas Aristidou</foaf:name>
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        <foaf:name>Nadia Magnenat-Thalmann</foaf:name>
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    <dct:title>Salsa dance learning evaluation and motion analysis in gamied virtual reality environment</dct:title>
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    <dcat:keyword>Motion analysis</dcat:keyword>
    <dcat:keyword>salsa dance</dcat:keyword>
    <dcat:keyword>virtual reality</dcat:keyword>
    <dcat:keyword>human computer interaction</dcat:keyword>
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        <foaf:name>European Commission</foaf:name>
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    <dct:description>&lt;p&gt;Learning couple dance such as salsa is challenging as it requires to understand and assimilate all the dance skills (guidance, rhythm, style) correctly. Salsa is traditionally learned by attending a dancing class with a teacher and practice with a partner, the difficulty to access such classes though, and the variability of dance environment can impact the learning process. Understanding how people&lt;br&gt; learn using a virtual reality platform could bring interesting knowledge in motion analysis and can be the first step toward a complementary learning system at home. In this paper, we propose an interactive learning application in the form of a virtual reality game, that aims to help the user to improve its salsa dancing skills. The application was designed upon previous literature and expert discussion and has different components that simulate salsa dance: A virtual partner with interactive control to dance with, visual and haptic feedback, and a game mechanic with dance tasks. This application is tested on a two-class panel of 20 regular and 20&lt;br&gt; non-dancers, and their learning is evaluated and analyzed through the extraction of Musical Motion Features and the Laban Motion Analysis system. Both motion analysis frameworks were compared prior and after training and show a convergence of the profile of non-dancer toward the profile of regular dancers, which validates the learning process. The work presented here has profound implications for future studies of motion analysis, couple dance learning, and human-human interaction.&lt;/p&gt;</dct:description>
    <dct:description>This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.</dct:description>
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