节奏即舞者：为初学者设计交互式节奏反馈

中文摘要 AI 辅助

学习跳舞很容易让初学者不知所措，尤其是在没有舞蹈老师有效指导的情况下。现有的交互系统往往不能充分支持学习者的进步。我们通过引入SkeletonDance，研究了针对节奏保持的定向反馈如何交互式地支持新手舞者的练习。我们的设计基于运动学习理论，并通过与舞蹈教师的访谈进行概念化，遵循既定的教学策略。SkeletonDance自动检测节奏缺陷，并通过模仿拍手反馈（舞蹈课中常见的教学技巧）提供帮助。在我们的研究中，参与者报告说，SkeletonDance帮助他们重新建立了失去的节奏，并增强了练习时的信心，尤其是在初学者中。尽管在受控测试环节中，客观表现指标并未一致地证实这些效果。我们的工作强调，反馈可以支持新手舞者的主观练习体验，并展示了先前舞蹈经验如何调节这种极简、受教师启发的干预措施的客观有效性。

英文摘要

Learning how to dance can readily overwhelm beginners, especially without effective guidance from a dance teacher. Existing interactive systems often do not sufficiently support the learner's progress. We investigated how targeted feedback on rhythm keeping interactively supports dance practice for novice dancers by introducing SkeletonDance. Our design is grounded in motor learning theory and conceptualized through interviews with dance teachers, following established teaching strategies. SkeletonDance automatically detects rhythm flaws and provides assistance through mimicking clapping feedback, a common instructional technique in dance lessons. In our study, participants reported that SkeletonDance helped them to re-establish lost rhythm and increased confidence during practice, especially among novices. Though objective performance metrics did not consistently confirm these effects during controlled test sessions. Our work highlights that feedback can support novice dancers' subjective practicing experiences and demonstrates how prior dancing experience moderates the objective effectiveness of such minimal, teacher-inspired interventions.

发表机构

• LMU Munich（慕尼黑大学）
• TU Wien（维也纳工业大学）
• German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
• RPTU Kaiserslautern-Landau（凯泽斯劳滕-兰道莱布尼茨理工大学）

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