Bodily expressed emotion understanding through integrating Laban movement analysis

TL;DR

This paper introduces a dataset and method for understanding emotions from body movements using Laban Movement Analysis, aiming to improve human-robot interaction, clinical diagnostics, and law enforcement applications.

Contribution

It develops a high-quality dataset based on Laban Movement Analysis and proposes a joint learning approach for motor elements and emotions.

Findings

Created a comprehensive human motor element dataset.

Demonstrated the feasibility of emotion recognition from body language.

Laid groundwork for interdisciplinary emotion analysis through movement.

Abstract

Body movements carry important information about a person's emotions or mental state and are essential in daily communication. Enhancing the ability of machines to understand emotions expressed through body language can improve the communication of assistive robots with children and elderly users, provide psychiatric professionals with quantitative diagnostic and prognostic assistance, and aid law enforcement in identifying deception. This study develops a high-quality human motor element dataset based on the Laban Movement Analysis movement coding system and utilizes that to jointly learn about motor elements and emotions. Our long-term ambition is to integrate knowledge from computing, psychology, and performing arts to enable automated understanding and analysis of emotion and mental state through body language. This work serves as a launchpad for further research into recognizing emotions through analysis of human movement.