MoveFeel: Expressive Dance Movement Determination through Video Analysis

Abstract

We propose MoveFeel, a movement computing framework that leverages vision-based analysis to compute meaningful metrics for assessing expressive dance movement. Our system is a multi-component workflow which extracts and collects dance movement images, processes and quantifies human pose skeletons, and computes attributes pertaining to dance movement that adhere to the movement methodologies based on "The Dynamics of Movement"by Rudolph Laban. We conduct a feasibility study to classify the expressive intentions of dance phrases encapsulated within a dance routine. We detail the interacting components of our system and discuss the interpretation and conversion of subjective dance principles into quantified metrics. MoveFeel demonstrates promise in generating metrics that can effectively distinguish dance phrases that are associated with positively or negatively expressed emotion. Our goal is to build upon these techniques and apply them to the movement arts (dance, theatre), medicine (therapy), and education.