HR‐HAR: A hierarchical relation representation for human activity recognition based on Wi‐Fi - Yanglin Pu; Yongqiang Jiang; Hai‐Miao Hu (IET Communications, 2023)
Abstract The Wi‐Fi‐based human activity recognition shows immense potential, as it is device‐free, non‐intrusive to privacy, and low‐cost. However, current learning‐based recognition methods mostly adopt the hybrid representation without distinguished contributions of features to different activities, which will be seriously affected by environment variations and interference of other persons, and costly to extend to new activities. Therefore, this paper proposes HR‐HAR, a hierarchical relation representation for human activity recognition, to improve the performance, extensibility, and robustness by exploiting the hierarchical relation of features of activities. The hierarchical relation reflects the different contributions of features to recognize different activities and effectively distinguishes similar activities. It naturally leads to a layered structure that can be extended to new activities without re‐training the entire model. With the layered structure, HR‐HAR first detects the existence of other persons and then processes un‐interfered scene and interfered scene signals with different methods, so it is robust to the interference. The experimental results on the public dataset with 95.6% accuracy and on the self‐collected dataset with 95.4% accuracy for un‐interfered scene and 95.0% for interfered scene indicate that HR‐HAR is of reliable performance on human activity recognition and is robust to environmental changes and interference of other persons.