Analysis and evaluation of dancing movement based on LMA

Abstract

The final goal of this research is to extract characteristic poses as well as highlight parts from data of dancing movement obtained by motion capturing technique. For this, the theory of Laban Movement Analysis (LMA) has been applied, and the physical feature values corresponding to the LMA components are defined. By observing the change over time of these feature values, body movements corresponding to the LMA components are extracted. In this paper we will mainly focus on Effort and Shape components of LMA. Results have been compared with those which have been extracted by a LMA specialist.

Laban movement analysis and hidden Markov models for dynamic 3D gesture recognition

“…In [25], Hachimura et al implement similar descriptors. The processed results are compared to specialists' annotation, and the matching occurs only for certain qualities.…”

“…For a given motion clip, key motion states are extracted, which represent its most salient properties. Let us finally quote the work of Samadani et al [27] who inspired from [28] [24] and [25] to propose different Laban features quantifications, and apply their descriptors to pre-defined gestures involving hands and head, designed by motion professionals and annotated both in terms of LMA factors (on 5-point Likert scales) and emotions (six categories). "Weight" and "Time" LMA dimensions show high correlation coefficients between annotations and quantification, which allows representing each emotion in the space generated by these two qualitative dimensions.…”

Musical Instruments, Body Movement, Space, and Motion Data: Music as an Emergent Multimodal Choreography

“…They can be achieved, for example, by calculating the area of bounding shapes (Glowinski et al, 2011), using the contraction index (Fenza et al, 2005) or measuring the volume of a convex hull that encloses the body (Hachimura, Takashina, & Yoshimura, 2005). When wearing two 9DoF sensors on the forearm, it is possible to project the orientation values over a hypothetical 2D plane in front of the subject and thus obtain approximate coordinates of the points in the plane the arms are pointing to.…”

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“…They can be achieved, for example, by calculating the area of bounding shapes , using the contraction index (Fenza et al, 2005) or measuring the volume of a convex hull that encloses the body (Hachimura, Takashina, & Yoshimura, 2005). When wearing two 9DoF sensors on the forearm, it is possible to project the orientation values over a hypothetical 2D plane in front of the subject and thus obtain approximate coordinates of the points in the plane the arms are pointing to.…”