Using machine learning to optimize the personalized training scheme of intelligent fitness equipment

Abstract

 Intelligent fitness equipment can monitor the user's movement state and body index in real time by integrating sensors and human-computer interaction interface, and provide data support for personalized training. In this study, the Gradient Boosting Decision Trees (GBDT) algorithm in machine learning is adopted, and a model is constructed to generate a personalized training scheme by combining multi-dimensional information such as the user's physical indicators, sports performance and feedback. The experimental results show that the prediction accuracy of the model on training data is over 90%, and it can be dynamically adjusted according to real-time data and feedback from users, which significantly improves  training effect and user satisfaction. The experimental results show that the personalized training scheme based on machine learning can comprehensively consider the multi-dimensional information of users, realize dynamic adjustment, and continuously improve the quality of training services with the learning and optimization of the model. This study not only promotes the development of intelligent fitness equipment, but also provides new ideas and methods for personalized service in the fitness industry.