Bodily expressed emotion understanding through integrating Laban movement analysis
This work addresses the need for improved emotion understanding in assistive robots, psychiatric diagnostics, and law enforcement, though it is incremental as it serves as a launchpad for further research.
The study tackled the problem of machine understanding of emotions expressed through body language by developing a high-quality human motor element dataset based on Laban Movement Analysis and using it to jointly learn motor elements and emotions, with the result being a foundational step toward automated emotion recognition from body movements.
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.