Audio-visual feedback for self-monitoring posture in ballet training

An application for ballet training is presented that monitors the posture position (straightness of the spine and rotation of the pelvis) deviation from the ideal position in real-time. The human skeletal data is acquired through a Microsoft Kinect v2. The movement of the student is mirrored through an abstract skeletal figure and instructions are provided through a virtual teacher. Posture deviation is measured as torso misalignment, via comparing hip center joint, shoulder center joint and neck joint position with an ideal posture position retrieved through initial calibration, and pelvis deviation, expressed as the xz-rotation of the hipcenter joint. The posture deviation is sonified via a varying cut-off frequency of a high-pass filter applied to floating water sound. The posture deviation is visualized via a curve and a rigged skeleton in which the misaligned torso parts are color-coded. In an experiment with 9-12 year-old dance students from a ballet school, comparing the audio-visual feedback modality with no feedback leads to an increase in posture accuracy (p < 0.001, Cohen’s d = 1.047). Reaction card feedback and expert interviews indicate that the feedback is considered fun and useful for training independently from the teacher. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Copyright remains with the author(s). NIME’17, May 15-19, 2017, Aalborg University Copenhagen, Denmark.