Convolutional neural network-based activity monitoring for indoor localization

Abstract

Abstract Location specific services are widely used in outdoor environment and their indoor counterpart is gaining more popularity as well. There is no standardized technology exists for indoor localization, usually smart phone is used as a localization platform and the field strength of an existing radio frequency infrastructure is used as the location specific information. Smart devices are also equipped with several sensors capable of capturing the motion data of the device. Detecting the walking step, turn, stairs motion type can refine the indoor position using digital indoor map as a reference. The real-time recognition of the motion type is possible with a precisely constructed and trained convolutional neural network and therefore it can improve the stability of the localization.