Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network †

In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research.