Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (4): 38-51.doi: 10.6040/j.issn.1672-3961.0.2025.131

• Machine Learning & Data Mining • Previous Articles    

A dual dynamic weighted ensemble method for personalized human activity recognition

Hu Lisha1, Gao Haichen1, Wang Suzhen2*   

  1. Hu Lisha1, Gao Haichen1, Wang Suzhen2*(1. School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, Hebei, China;
    2. Department of Scientific Research and Industry-Education Collaboration, Hebei University of Engineering Science, Shijiazhuang 050091, Hebei, China
  • Published:2026-08-12

Abstract: Aiming at the problem that traditional static models could not effectively address the personalization and dynamic changes in human activity recognition, a dual dynamic weighted ensemble(DDWE)method for personalized human activity recognition was proposed. By integrating multiple concept drift detection techniques with adaptive mechanisms of base classifiers, real-time detection and adaptive learning of user personalized activities in dynamic data streams were enabled. A weight matrix updating strategy based on class probability and KappaM was designed to dynamically adjust the weights of base classifiers from the perspectives of class confidence and recognition ability, improving the adaptability and accuracy of the ensemble classifier in user-specific recognition tasks. Experiments conducted on several public human activity recognition datasets showed that DDWE method outperformed existing approaches and demonstrated clear advantages in personalized human activity recognition, providing guidance and reference for related fields such as medical monitoring, sports rehabilitation, and intelligent elderly care.

Key words: human activity recognition, personalized, data stream, ensemble learning, dynamic

CLC Number: 

  • TP181
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