Classification of Salsa Dance Level using Music and Interaction based Motion Features

Abstract

Learning couple dance such as Salsa is a challenge for the modern human as it requires to assimilate and understand correctly all the dance parameters. Traditionally learned with a teacher, some situation and the variability of dance class environment can impact the learning process. Having a better understanding of what is a good salsa dancer from motion analysis perspective would bring interesting knowledge and can complement better learning. In this paper, we propose a set of music and interaction based motion features to classify salsa dancer couple performance in three learning states (beginner, intermediate and expert). These motion features are an interpretation of components given via interviews from teacher and professionals and other dance features found in systematic review of papers. For the presented study, a motion capture database (SALSA) has been recorded of 26 different couples with three skill levels dancing on 10 different tempos (260 clips). Each recorded clips contains a basic steps sequence and an extended improvisation sequence during two minutes in total at 120 frame per second. Each of the 27 motion features have been computed on a sliding window that corresponds to the 8 beats reference for dance. Different multiclass classifier has been tested, mainly k-nearest neighbours, Random forest and Support Vector Machine, with an accuracy result of classification up to 81% for three levels and 92% for two levels. A later feature analysis validates 23 out of 27 proposed features. The work presented here has profound implications for future studies of motion analysis, couple dance learning and human-human interaction.

Salsa dance learning evaluation and motion analysis in gamified virtual reality environment

“…All measurements are observed on a temporal window of given frames corresponding to 8 beats. Previous experiments, e.g., [40], show that 25 frames per second are the best to extract meaningful results. Thus, we downsampled the initial frame rate (100Hz) to 25 frames per second (fps) without loss of the temporal information (see Forbes and Fiume [17]).…”

“…Another work [1], elaborated upon the expressive qualities, such as rigidity, fluidity, and impulsiveness, to investigate intra-personal synchronization for full-body movement classification. In our previous study [41,40], we propose a set of motion features that take into account the particular context of salsa dance: motion synchronized with music and interaction with other partners.…”

“…A layer of inverse kinematics with physical constraints (bending of the upper body and other limbs) is added to the rigged model, allowing to manipulate the end-effectors with ease, achieving constituent motion. The knowledge of basic salsa step's motion in space comes from a previous study [41,40], from which we extract a motion profile for each foot, as illustrated in figure 3. This motion profile serves as a base to set the position in space of the IK targets corresponding to the right and left foot of the Virtual Partner (VP).…”

“…force is applied and the direction of the force (Sides or front). A valid gesture for transition is considered if: The direction of force is perpendicular to the direction of the current dance pattern, and if the force occurs between the beat 7 and 8 (in a similar manner to [40]). The results give the user the feeling of guiding the VP, as illustrated in figure 5.…”

“…Salsa is a specific type of dance in which movements are highly correlated with the music and the other partner. To take that into consideration, we have previously proposed the MMF framework [41,40], which contains the relevant motion features. MMF indicates excellent performance in classifying motion data with regard to three essential salsa dance skills: rhythm, guidance, and style.…”