Abstract
Background: In the fields of rehabilitation, public health, military training and other domains, the accurate and effective monitoring of exercise intensity during exercise can control the occurrence of sports injuries, which is of great significance for people's healthy lives. Objective: This study combined easily collectable multi-dimensional sensor data and various algorithm models to achieve cross-individual recognition of low, middle and high levels of exercise intensity. Methods: This study compared the recognition performance of different algorithm models using acceleration and angular velocity sensors worn on seven body parts through individualised body data characteristics. Results: The recognition performances of two classical machine learning algorithms were the worst, with a recognition rate of only 82.97% and 80.31%. The performances of two ensemble learning algorithms were slightly better, with a recognition rate of 88.86% and 87.35%. The deep sub-domain adaptation network algorithm proposed in this study exhibited the best performance, with a recognition rate of 92.87%. This study utilised multi-dimensional sensors to construct a cross-individual exercise intensity recognition model for different parts of the body, and the overall recognition rate of the left part was higher than that of the right part. Moreover, the recognition effect upon wearing sensors on the left side of the body is better than the right in running events. Conclusions: The results of this study have demonstrated the effectiveness of combining domain adaptation methods and multi-dimensional sensors for cross-individual exercise intensity recognition, laying a solid theoretical foundation for broader-scale cross-individual exercise intensity recognition in future research.