{"id":5689,"date":"2025-10-15T14:05:51","date_gmt":"2025-10-15T05:05:51","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=5689"},"modified":"2025-12-05T15:31:23","modified_gmt":"2025-12-05T06:31:23","slug":"contents-e004","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e004\/","title":{"rendered":"Predicting Athlete Injuries with Multi-Sigma\u00ae"},"content":{"rendered":"\n

Using Multi-Sigma\u00ae, we present a case study that analyzes the relationship between athletes\u2019 subjective load and injury occurrence through its AI Chain Analysis feature.<\/h1>\n\n\n\n
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1\uff0eBuilding an AI model for subjective load and injury occurrence using AI Chain Analysis\u3000<\/h2>\n\n\n\n

Using Multi-Sigma\u00ae and its AI Chain Analysis feature, we analyzed training logs recorded between 2012 and 2019 from a Dutch track-and-field team to derive insights for injury prevention. From 74 middle- and long-distance runners (47 men, 27 women), we built one AI model to predict subjective load<\/span> from 62 training-related variables, and a second AI model to predict injury occurrence<\/span> from 63 variables, including subjective load<\/span>.<\/p>\n\n\n\n

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\"Building<\/figure>\n\n\n\n
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2.\u3000Prediction of Subjective Load and Injury Occurrence via AI Chain Analysis\u3000<\/h2>\n\n\n\n
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\"Using<\/figure>\n\n\n\n
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3.\u3000Contribution Analysis with AI Chain Analysis\u3000<\/h2>\n\n\n\n

\n Using the contribution analysis feature, we examined the factors influencing subjective load<\/span> and injury occurrence<\/span>.\n As a result, high-intensity training and cross-training were identified as particularly important factors.\n<\/p>\n\n\n\n

\"Contribution<\/figure>\n\n\n\n
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This analytical framework can serve as a practical tool for athlete condition management and injury prevention. It is well suited for automated day-to-day monitoring of training load and for the early detection of injury risk.<\/p>\n\n\n\n

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(Note1)Note: The data used in this analysis was processed and edited based on the data published in the article below, under Creative Commons Zero 1.0 Universal (CC0 1.0) license.
Dataset: https:\/\/www.kaggle.com\/datasets\/shashwatwork\/injury-prediction-for-competitive-runners\/data
(Note2) We used 1,000 training samples and 166 test samples.<\/p>\n\n\n\n

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