Indoor Positioning System: Improved deep learning approach based on LSTM and multi-stage activity classification

Abstract

Pedestrian dead reckoning (PDR) for indoor localization has the privilege of utility without prior environment knowledge and adjunct infrastructure. It can be a potentially scalable solution for various indoor positioning, visually impaired people navigation aide, and commercial applications. In this study, we develop a multi-stage deep learning-based approach to detect and estimate the stride and heading of a user. This approach takes advantage of classifying user action units from inertial sensors of smartphone and relevant action units and is then separately processed to estimate user displacement in terms of displacement distance and direction, respectively, with automatic feature extraction. The proposed system provides improved performance over the preceding deep learning model. It also exhibits two-dimensional finer resolution maneuvering of the user in contrast to only the left and right turn. Experiments were conducted to train and evaluate the proposed system's performance, the results of which validate the improved utility of our deep learning-based system.