Tutorial on Movement Notation: An Interdisciplinary Methodology for HRI
to Reveal the Bodily Expression of Human Counterparts via
Collecting Annotations from Dancers in a Shared Data Repository

Amy LaViers (1), Cat Maguire (2), James Z. Wang (3), Rachelle Tsachor (4)
(1) The Robotics, Automation, and Dance Lab, Philadelphia, PA, USA
(2) WholeMovement, Palmyra, VA, USA
(3) The Pennsylvania State University, University Park, PA, USA
(4) University of Illinois, Chicago, IL, USA

Abstract:

How do we make a machine that indicates changes to its internal state, e.g., status, goals, attitude, or even emotion, through changes in movement profiles? This workshop will pose a possible direction toward such ends that leverages movement notation as a source for clearly defining abstract concepts of similarity and symbolic representation of the parts and patterns of movement - in order to identify, record and interpret patterns of human movement on both the micro and macro levels. First, we will move together. This will activate an innate ability to imitate each other and, in doing so, illuminate the principal components of Laban/Bartenieff Movement Studies – a field comprised of Laban Movement Analysis and Bartenieff Fundamentals – and the Body, Effort, Shape, Space, and Time (BESST) System of movement analysis. This system of work, deriving from dance and physical therapy practices, which is a textbook; thus, a key value proposition of the workshop is in its embodied, situated nature that can be supplemented by textbooks, including a newly released book from MIT Press authored by the workshop organizers. Next, we will try to write down what we’re doing. A set of symbols for describing elements of the BESST System, which seem to be particularly perceptually meaningful to human observers, will be presented so that movement ideas can be notated and, thus, translated between bodies. We will explore both Labanotation and a related “motif”-style notation. This workshop is supported by NSF grant numbers 2234195 and 2234197.


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Citation: Amy LaViers, Cat Maguire, James Z. Wang and Rachelle Tsachor, ``Tutorial on Movement Notation: An Interdisciplinary Methodology for HRI to Reveal the Bodily Expression of Human Counterparts via Collecting Annotations from Dancers in a Shared Data Repository,'' Proceedings of the Annual ACM/IEEE International Conference on Human Robot Interaction Companion, pp. - , 2024.

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Last Modified: February 26, 2024
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