Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors

Abstract

Human activity recognition (HAR) has been applied to several advanced applications, especially when individuals may need to be monitored closely. This work focuses on HAR using wearable sensors attached to various locations of the user body. The data from each sensor may provide unequally discriminative information and, then, an effective fusion method is needed. In order to address this issue, inspired by the squeeze-and-excitation (SE) mechanism, we propose the merging-squeeze-excitation (MSE) feature fusion which emphasizes informative feature maps and suppresses ambiguous feature maps during fusion. The MSE feature fusion consists of three steps: pre-merging, squeeze-and-excitation, and post-merging. Unlike the SE mechanism, the set of feature maps from each branch will be recalibrated by using the channel weights also computed from the pre-merged feature maps. The calibrated feature maps from all branches are merged to obtain a set of channel-weighted and merged feature maps which will be used in the classification process. Additionally, a set of MSE feature fusion extensions is presented. In these proposed methods, three deep-learning models (LeNet5, AlexNet, and VGG16) are used as feature extractors and four merging methods (addition, maximum, minimum, and average) are applied as merging operations. The performances of the proposed methods are evaluated by classifying popular public datasets.