Seeing, Sensing and Recognizing Laban Movement Qualities

Abstract : Human movement has historically been approached as a functional component of interaction within human computer interaction. Yet movement is not only functional, it is also highly expressive. In our research, we explore how movement expertise as articulated in Laban Movement Analysis (LMA) can contribute to the design of computational models of move-ment's expressive qualities as defined in the framework of Laban Efforts. We include experts in LMA in our design process, in order to select a set of suitable multimodal sensors as well as to compute features that closely correlate to the definitions of Efforts in LMA. Evaluation of our model shows that multimodal data combining positional, dynamic and physiological information allows for a better characterization of Laban Efforts. We conclude with implications for design that illustrate how our methodology and our approach to multimodal capture and recognition of Effort qualities can be integrated to design interactive applications.
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Communication dans un congrès
ACM Conference on Human Factors in Computing Systems (CHI), May 2017, Denver, United States. 〈10.1145/3025453.3025530〉
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https://hal.inria.fr/hal-01663132
Contributeur : Sarah Fdili Alaoui <>
Soumis le : jeudi 21 décembre 2017 - 17:06:43
Dernière modification le : jeudi 22 novembre 2018 - 14:34:16

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Sarah Alaoui, Jules Françoise, Thecla Schiphorst, Karen Studd, Frédéric Bevilacqua. Seeing, Sensing and Recognizing Laban Movement Qualities. ACM Conference on Human Factors in Computing Systems (CHI), May 2017, Denver, United States. 〈10.1145/3025453.3025530〉. 〈hal-01663132〉

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