A Lightweight Deep Convolutional Neural Network with Squeeze-and-Excitation Modules for Efficient Human Activity Recognition using Smartphone Sensors

Abstract

Human activity recognition (HAR) relying on wearable sensors has become a new challenging area of research in pervasive and ubiquitous computing due to the rapid advancements in sensor technology. Since several deep learning (DL) networks have been introduced to handle the problem of feature extraction in machine learning, these techniques have recently garnered considerable attention. Nevertheless, most recent DL networks utilize sensory data by automatically extracting spatial properties without addressing cross-channel data at the same level. This information from each sensor channel was independently conveyed in a hierarchical method from shallow levels to deeper levels. This paper introduces SE-DeepConvNet, a lightweight deep convolutional neural network with squeeze-and-excitation modules for recognizing human activity from smartphone sensor data. The recommended SE-DeepConvNet was developed and assessed on the UCI-HAR dataset, a public benchmark HAR dataset. According to the results obtained, the SE-DeepConvNet outperforms other baseline DL networks with a maximum accuracy of 99.27%.