This research aims to use generative artificial intelligence (Generative AI) and healthcare auxiliary technology to build a high-intensity interval training (HIIT) feature optimization and personalized generation framework. HIIT is receiving more and more attention as an efficient fitness training model. However, traditional methods lack personalized guidance and scientific optimization, making it difficult to give full play to its potential effectiveness. To this end, the study first systematically collected biomedical data, sports biomechanical data and personal baseline data during HIIT training. Secondly, perform preprocessing such as standardization and dimensionality reduction on heterogeneous multi-source features, and use algorithms such as feature cascading and selection to achieve optimal integration and obtain high-quality comprehensive feature representation. Based on integrated feature training, the HIIT generation model integrates supervised learning and unsupervised learning, which can automatically identify training actions, predict physiological responses, and capture individual differences to generate a scientific training program tailored for each individual in order to improve training accuracy. It has excellent performance in terms of personalization level and interpretability, and provides a new theoretical basis and technical means to promote public health quality.

Min Fan. Research on high-intensity training interval feature integration guided by generative artificial intelligence assisted by medical care. Frontiers in Sport Research (2024) Vol. 6, Issue 4: 56-62. https://doi.org/10.25236/FSR.2024.060409.

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