Wesely Sophia, Hofer Ella, Curth Robin, Paryani Shyam, Mills Nicole, Ueberschär Olaf, Westermayr Julia
Institute of Physical and Theoretical Chemistry, Faculty of Chemistry, Leipzig University, 04103 Leipzig, Germany.
Department of Engineering and Industrial Design, Magdeburg-Stendal University of Applied Sciences, 39110 Magdeburg, Germany.
Sensors (Basel). 2025 Apr 3;25(7):2260. doi: 10.3390/s25072260.
Over the past four decades, cheerleading evolved from a sideline activity at major sporting events into a professional, competitive sport with growing global popularity. Evaluating tumbling elements in cheerleading relies on both objective measures and subjective judgments, such as difficulty and execution quality. However, the complexity of tumbling-encompassing team synchronicity, ground interactions, choreography, and artistic expression-makes objective assessment challenging. Artificial intelligence (AI) revolutionised various scientific fields and industries through precise data-driven analyses, yet their application in acrobatic sports remains limited despite significant potential for enhancing performance evaluation and coaching. This study investigates the feasibility of using an AI-based approach with data from a single inertial measurement unit to accurately identify and objectively assess tumbling elements in standard cheerleading routines. A sample of 16 participants (13 females, 3 males) from a Division I collegiate cheerleading team wore a single inertial measurement unit at the dorsal pelvis. Over a 4-week seasonal preparation period, 1102 tumbling elements were recorded during regular practice sessions. Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. Our results indicate that certain machine learning models can effectively identify different tumbling elements with high accuracy despite inter-individual variability and data noise. These findings demonstrate the significant potential for integrating AI-driven assessments into cheerleading and other acrobatic sports in order to provide objective metrics that complement traditional judging methods.
在过去的四十年里，啦啦队运动已经从大型体育赛事的一项边缘活动发展成为一项全球性日益普及的专业竞技运动。评估啦啦队运动中的翻腾动作既依赖于客观测量，也依赖于主观判断，比如难度和执行质量。然而，翻腾动作的复杂性，包括团队同步性、与地面的互动、编排和艺术表现力，使得客观评估具有挑战性。人工智能通过精确的数据驱动分析彻底改变了各个科学领域和行业，然而尽管其在提升表现评估和指导方面具有巨大潜力，但其在杂技运动中的应用仍然有限。本研究调查了使用基于人工智能的方法结合来自单个惯性测量单元的数据来准确识别和客观评估标准啦啦队套路中翻腾动作的可行性。来自一所一级大学啦啦队的16名参与者（13名女性，3名男性）在背部骨盆处佩戴了一个惯性测量单元。在为期4周的赛季准备期内，在常规训练中记录了1102个翻腾动作。利用三轴加速度和转速，采用各种机器学习算法对翻腾动作的执行进行分类和评估。我们的结果表明，尽管存在个体差异和数据噪声，某些机器学习模型仍能有效地高精度识别不同的翻腾动作。这些发现表明，将人工智能驱动的评估整合到啦啦队运动和其他杂技运动中具有巨大潜力，以便提供补充传统评判方法的客观指标。
