Monitoring Human Induced Floor Vibrations for Quantifying Dance Moves: A Study of Human–Structure Interaction

Abstract

Human induced dynamic forces on structures are of interest in the area of human-environment interfaces. The research community is interested in characterizing human decisions and providing information on the consequences of human actions to control those human forces more effectively. Dynamic structures tend to vibrate when subjected to human induced motion. In the context of human-structure interactions, dance induced vibrations that are difficult to quantify solely by human perception can be quantified with sensors. This data can provide a unique opportunity for dancers to understand the quality of their dance with objective metrics. Previous work in capturing dance moves required wearable sensors attached to dancers’ body. Often an intrusive process, this method is not scalable if dancers are not familiar with technology and it limits their participation without access to special studios or facilities. If simple, deployable technology could be available to dancers, they could monitor their dance without engineers. This research integrates dancers’ interest in qualifying dance motion and engineering curiosity to study human induced vibrations. As a part of the framework, researchers used two indices that can differentiate a well synchronized group dance from asynchronous moves. The indices were derived from measurements of the movement of the structure dynamically excited by the dancers, hence quantifying dance coordination. These are the Harmony Index and Coordination Index, respectively. These two indices are based on time history data obtained from sensors installed on a wooden bridge where dancers performed at different levels of proficiency. The two indices obtained from the sensors are validated against the Visual Index, a qualitative index obtained from an expert who judged dance moves based on one video capture. The results of this research showed that the two indices quantify effectively the quality of the dancers, validated with the Visual Index. As a result, this research proposes using Low-cost Efficient Wireless Intelligent Sensor (LEWIS) to objectively sort different levels of dance quality. The future application of this human-centered sensing proposes sensing other types of human decisions using sensors, which contributes to quantify objectively human-infrastructure interfaces.