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.
Full color PDF file (1.1MB)
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.
Copyright 2024 Authors. Personal use of this material is
permitted. However, permission to reprint/republish this material for
advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse
any copyrighted component of this work in other works, must be
obtained from Authors.
Last Modified:
February 26, 2024
© 2024