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

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

Blog Article

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 model opla 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 superhero lollipops 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.