DanceTutor: An ITS for Coaching Novice Ballet Dancers Using Pose Recognition of Whole-Body Movements

AIED (2)(2021)

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This paper presents the design, development and evaluation of a prototype intelligent dance tutoring system, DanceTutor, for coaching students in low-resource settings. The system evaluates seventeen core body points on a dancer using video footage captured from a mobile phone or web camera using a combination of simple algorithms and 2D pose estimation software. Detailed feedback is provided on the quality and correctness of the dancer’s pose for the first five static dance positions in Ballet, and then for intermediate to advanced exercises with permutations of the five basic Ballet positions. Evaluation of the prototype revealed the highly subjective nature and cultural biases of evaluating the quality of a dancer’s technique. Three experienced dance teachers, trained in different countries, evaluated 165 video recordings of 11 candidate dancers. The system was only able to achieve 47% consensus overall with the feedback and grading results produced by the dance teachers, who each evaluated tension and height differently. There was however a 60% agreement between DanceTutor and one teacher who used the most granular evaluation strategy matching DanceTutor’s baseline and assessment features.
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Key words
Intelligent tutoring system,Ballet,Gesture recognition,Student modelling,Whole-body movement,Feedback
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