Plug-and-play multi-dimensional attention module for accurate Human Activity Recognition

Abstract

Unlike image data, Human Activity Recognition (HAR) data is collected through sensors placed on different parts of the body. Each sensor may have varying importance for different human actions. Attention mechanisms can dynamically identify the significance of signals from different sensors. However, existing attention modules often operate along channel or spatial dimensions, producing one-dimensional or two-dimensional weights, and considering neurons within every channel or spatial location as equivalent. This might constrain their capacity to acquire more distinctive cues. In this paper, we present a novel Three-Dimensional Weight Attention Module (WAM) that considers both spatial and weight information of each channel. Specifically, we commence by introducing an energy function and subsequently assess the significance of each neuron through optimization. Unlike conventional channel or spatial attention modules, this module can compute 3D attention weights for feature maps without additional parameters. Experiments conducted on four diverse HAR datasets showcase that the Three-Dimensional Weight Attention Module can seamlessly integrate with convolutional models, significantly enhancing model performance without introducing extra computational burden. To further highlight the utility of WAM, we measure the actual inference time on an embedded platform.