Analysis of Impression of Robot Bodily Expression

Abstract

A set of physical feature values is proposed in order to explain impression produced by bodily expression. The concept of designing the value set is based on Laban Movement Analysis which is a famous theory in body move-ment psychology. Impressions produced by body expression are closely related to these feature values. Each of the feature values is defined mathematically, so that it is easy to implement in a computer. Also the feature values are calculated on general body movements. Therefore, the set can be implemented in body expression systems of robots and computer graphics bodies in order to facilitate quantitative evaluation and forecast of impression produced by their bodies. This paper describes the concept and an example of implementation on a robot. Validity of the set is verified in an experiment.

Laban Effort and Shape Analysis of Affective Hand and Arm Movements

“…There have been a few attempts to quantify Laban components. Nakata et al proposed a quantification of the Effort components (Time, Weight, Space), and Shape components (Shaping/Carving and Shape Flow) for a pet robot [12]. The quantified components were used to generate different dance movements, which were perceived by human observers as conveying distinct emotions in a user study [12].…”

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

“…In such cases, the expressive characterization is directly determined as a function of dynamic features, and is compared to the annotation carried out by experts. In [24], Nakata et al propose a set of motion descriptors, each one referring to a LMA component, and apply these descriptors to dancing robots gestures annotated with the help of four emotional categories. Factor analysis is used to establish causality between Laban qualities and emotions.…”

“…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.…”

Affective gesturing with music mood recognition

“…Lourens et al enabled a robot to identify the emotional content of a user based on their gestures [11] by modeling the emotions with Laban notation, a type of notation used specifically to record dances [12]. Nakata independently verified that robots are capable of displaying emotion by following Laban principles [13].…”