Dance motion recognition and correction present unique challenges due to the subtle distinctions in movement execution that differentiate correct performance from flawed attempts. This paper proposes an integrated system combining 3D motion capture technology with a novel spatiotemporal attention-based graph convolutional network for accurate dance action recognition and intelligent feedback generation. The proposed architecture features a dual-stream design incorporating adaptive graph topology learning that discovers task-relevant relationships between non-adjacent joints, alongside multi-scale temporal modeling to capture movement dynamics across varying time scales. A multi-dimensional correction feedback algorithm translates recognition outputs into prioritized, actionable guidance by comparing performer movements against professional reference templates through dynamic time warping alignment and joint-level deviation analysis. We constructed DanceMotion-86, a comprehensive dataset comprising 10,836 clips across 86 action categories spanning five dance genres. Experimental results demonstrate that the proposed method achieves 92.3% recognition accuracy, outperforming state-of-the-art baseline methods. User studies with 36 participants confirmed 87.4% error detection rate and showed significantly accelerated skill acquisition among feedback-enabled learners compared to control conditions. The system offers practical applications for intelligent dance instruction, cultural heritage preservation, and remote training platforms.
Wang et al. (Wed,) studied this question.
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