Comparing Cross-Subject Performance on Human Activities Recognition Using Learning Models

Human activities recognition (HAR) plays a vital role in fields like ambient assisted living and health monitoring, in which cross-subject recognition is one of the main challenges coming from the diversity of various users. Although recent studies have achieved satisfactory results in a non-cross-subject condition, the recognition performance has significant degradation under the cross-subject criterion. In this paper, we evaluate three traditional machine learning methods and five deep neural network architectures under the same metrics on three popular HAR datasets: mHealth, PAMAP2, and UCIDSADS. The experimental results show that traditional machine learning approaches are generally more robust to the new subject scenarios under strict leave-one-subject-out cross-validation. Extra analysis indicates that hand-crafted features are one major reason for the better performance of traditional machine learning on cross-subject HAR, while deep learning is more prone to learning subject-dependent features under an end-to-end training process. A novel training strategy for decision-tree-based methods is also proposed in this paper, resulting in an improvement on the random forest model which achieves competitive performance at an average F1-score (accuracy) of 94.49% (95.09%), 91.64% (92.21%), and 92.70% (93.29%) on the three datasets, compared with state-of-the-art solutions for cross-subject HAR.