Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks

Abstract

Human physical activity recognition based on wearable sensors has applications relevant to our daily life such as healthcare. How to achieve high recognition accuracy with low computational cost is an important issue in the ubiquitous computing. Rather than exploring handcrafted features from time-series sensor signals, we assemble signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to automatically learn the optimal features from the activity image for the activity recognition task. Our proposed approach is evaluated on three public datasets and it outperforms state-of-the-arts in terms of recognition accuracy and computational cost.