Motion indexing of different emotional states using LMA components

Computer Graphics Forum

Charalambous

Chrysanthou

2015

Self Cite

The increasing availability of large motion databases, in addition to advancements in motion synthesis, has made motion indexing and classification essential for better motion composition. However, in order to achieve good connectivity in motion graphs, it is important to understand human behaviour; human movement though is complex and difficult to completely describe. In this paper, we investigate the similarities between various emotional states with regards to the arousal and valence of the Russell's circumplex model. We use a variety of features that encode, in addition to the raw geometry, stylistic characteristics of motion based on Laban Movement Analysis (LMA). Motion capture data from acted dance performances were used for training and classification purposes. The experimental results show that the proposed features can partially extract the LMA components, providing a representative space for indexing and classification of dance movements with regards to the emotion. This work contributes to the understanding of human behaviour and actions, providing insights on how people express emotional states using their body, while the proposed features can be used as complement to the standard motion similarity, synthesis and classification methods.

Section: Literature Reviewmentioning

confidence: 99%

Emotion Analysis and Classification: Understanding the Performers' Emotions Using the LMA Entities

Computer Graphics Forum

Charalambous

Chrysanthou

2015

Self Cite

“…Studies on everyday actions [18] are proposing a set of features inspired by psychology and physiology to characterized behaviors and the subsequent emotion involved. More specifically, the use of LMA-based features has proved to work well in different situations, such as motion retrieval, indexing, and comparison [4,6], and is therefore ideal to be used as a base to build a machine learning classifier, as demonstrated for theatre emotional expression [39] or evaluating the performer's emotion using LMA features [3]. Other studies focused on a specific motion feature, for example, the fluidity of the movement that is a critical dance parameter investigated in [33].…”

Section: Related Workmentioning

confidence: 99%

Salsa dance learning evaluation and motion analysis in gamified virtual reality environment

Senecal

Nijdam

et al. 2020

Multimed Tools Appl

Self Cite

Learning couple dance such as salsa is challenging as it requires to understand and assimilate all the dance skills (guidance, rhythm, style) correctly. Salsa is traditionally learned by attending a dancing class with a teacher and practice with a partner, the difficulty to access such classes though, and the variability of dance environment can impact the learning process. Understanding how people learn using a virtual reality platform could bring interesting knowledge in motion analysis and can be the first step toward a complementary learning system at home. In this paper, we propose an interactive learning application in the form of a virtual reality game, that aims to help the user to improve its salsa dancing skills. The application was designed upon previous literature and expert discussion and has different components that simulate salsa dance: A virtual partner with interactive control to dance with, visual and haptic feedback, and a game mechanic with dance tasks. This application is tested on a two-class panel of 20 regular and 20 non-dancers, and their learning is evaluated and analyzed through the extraction of Musical Motion Features and the Laban Motion Analysis system. Both motion analysis frameworks were compared prior and after training and show a convergence of the profile of non-dancer toward the profile of regular dancers, which validates the learning process. The work presented here has profound implications for future studies of motion analysis, couple dance learning, and human-human interaction.

“…Kim et al uses Kinect and LMA to propose an emotional representation for robotics. In , Aristidou and Chrysanthou used a variety of LMA features to classify acted dance performances with different emotions, and they also provided a brief analysis of how these features change on movements with different emotions, finding movement similarities between the different emotional states. Some experiments like those of Valstar et al try to map automatic facial emotion recognition to emotion space.…”

Section: Previous Workmentioning

confidence: 99%

Continuous body emotion recognition system during theater performances

Senecal

Cuel

Computer Animation & Virtual

et al. 2016

Self Cite

Understanding emotional human behavior in its multimodal and continuous aspect is necessary for studying human machine interaction and creating constituent social agents. As a first step, we propose a system for continuous emotional behavior recognition expressed by people during communication based on their gesture and their whole body dynamical motion. The features used to classify the motion are inspired by the Laban Movement Analysis entities and are mapped onto the well‐known Russell Circumplex Model . We choose a specific case study that corresponds to an ideal case of multimodal behavior that emphasizes the body motion expression: theater performance. Using a trained neural network and annotated data, our system is able to describe the motion behavior as trajectories on the Russell Circumplex Model diagram during theater performances over time. This work contributes to the understanding of human behavior and expression and is a first step through a complete continuous emotion recognition system whose next step will be adding facial expressions. Copyright © 2016 John Wiley & Sons, Ltd.