An Enhanced Human Activity Recognition Algorithm with Positional Attention

Abstract

Human activity recognition (HAR) attracts widespread attention from researchers recently, and deep learning is employed as a dominant paradigm of solving HAR problems. The previous techniques rely on domain knowledge or attention mechanism extract long-range dependency in temporal dimension and cross channel correlation in sensor’s channel dimension. In this paper, a HAR model with positional attention (PA), termed as PA-HAR, is presented. To enhance the features in both sensor’s channel and temporal dimensions, we propose to split the sensor signals into two 1D features to capture the long-range dependency along the temporal-axis and signal’s cross-channel information along the sensor’s channel-axis. Furthermore, we embed the features with positional information by encoding the generated features into pairs of temporal-aware and sensor’s channel-aware attention maps and weighting the input feature maps. Extensive experiments based on five public datasets demonstrate that the proposed PA-HAR algorithm achieves a competitive performance in HAR related tasks compared with the state-of-the-art approaches.