Human activity recognition using wearable sensors by deep convolutional neural networks

Human physical activity recognition based on wearable sen-sors has applications relevant to our daily life such as health-care. 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 activ-ity image for the activity recognition task. Our proposed approach is evaluated on three public datasets and it out-performs state-of-The-Arts in terms of recognition accuracy and computational cost.