Seeing, Sensing and Recognizing Laban Movement Qualities

Explore a 22-minute conference talk from the ACM CHI Conference on Human Factors in Computing Systems that delves into the computational modeling of expressive movement qualities using Laban Movement Analysis (LMA). Learn how researchers incorporate movement expertise to design multimodal sensors and compute features correlating to Laban Efforts. Discover the importance of combining positional, dynamic, and physiological data for better characterization of movement qualities. Gain insights into the methodology for capturing and recognizing Effort qualities, and understand its potential applications in interactive design. The presentation covers the LMA framework, the significance of movement in HCI, computational systems for movement analysis, and the results of the research, concluding with a Q&A session.

Introduction
Laban Movement Analysis Framework
Why is Movement Important
Movement is Complex
Dance and Movement
Effort
Effort Actions
Laban Experts
Interview
Computational System
Quality of Recognition
Results
Contributions
Conclusion
Questions