Artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports

• URL: http://arxiv.org/abs/2503.04764v1
• Date: Tue, 11 Feb 2025 15:16:03 GMT
• Title: Artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports
• Authors: Sophia Wesely, Ella Hofer, Robin Curth, Shyam Paryani, Nicole Mills, Olaf Ueberschär, Julia Westermayr,
• Abstract summary: evaluating tumbling elements in cheerleading relies on both objective measures and subjective judgments.<n>The complexity of tumbling - encompassing team synchronicity, ground interactions, choreography, and artistic expression - makes objective assessment challenging.<n>This study investigates the feasibility of using an AI-based approach with data from a single inertial measurement unit.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past four decades, cheerleading has 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) has revolutionized 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. Results indicate that certain machine learning models can effectively identify different tumbling elements despite inter-individual variability and data noise, achieving high accuracy. These findings demonstrate the significant potential for integrating AI-driven assessments into cheerleading and other acrobatic sports, providing objective metrics that complement traditional judging methods.

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