CatBoost-based stacked ensemble model for human activity recognition

Abstract

Human Activity Recognition (HAR) is a challenging task that involves accurately classifying diverse daily movements from data captured by sensors, videos, or images. In this study, we propose a robust HAR framework that integrates CatBoost with a stacked ensemble learning (SEL) strategy, combining multiple base classifiers to enhance accuracy and generalization beyond conventional machine learning approaches. The framework was first evaluated on the benchmark WISDM, RealWorld and PAMAP2 datasets, comprising raw triaxial accelerometer signals segmented with a sliding window approach, demonstrating its effectiveness. The CatBoost model within the SEL framework achieved strong performance in identifying activities such as walking and jogging, while also delivering nearly perfect recognition for stair-related activities, with average scores of 87.06% accuracy, 89.25% recall, 79.93% precision, 84.26% F1-score, and 85.43% ROC-AUC across all WISDM activities. To assess generalization, the framework was further tested on the RealWorld HAR and PAMAP2 datasets. On RealWorld HAR, it achieved 99.2% accuracy, 99.06% recall, 99.23% precision, 99.13% F1-score, and 99.1% ROC-AUC, whereas on PAMAP2, it attained 99.43% accuracy, 99.33% recall, 99.53% precision, 99.43% F1-score, and 99.36% ROC-AUC. These results highlight the capability of ensemble learning combined with boosting methods to advance sensor-based HAR across multiple benchmark datasets, offering high reliability and generalization in real-world scenarios.