  Classifying a pedestrian in one of the three conveyor states of "elevator,"
"escalator" and "neither" is fundamental to many applications such as indoor
localization and people flow analysis. We estimate, for the first time, the
pedestrian conveyor state given the inertial navigation system (INS) readings
of accelerometer, gyroscope and magnetometer sampled from the phone. Our
problem is challenging because the INS signals of the conveyor state are
coupled and perturbed by unpredictable arbitrary human actions, confusing the
decision process. We propose ELESON, a novel, effective and lightweight
INS-based deep learning approach to classify whether a pedestrian is in an
elevator, escalator or neither. ELESON utilizes a motion feature extractor to
decouple the conveyor state from human action in the feature space, and a
magnetic feature extractor to account for the speed difference between elevator
and escalator. Given the results of the extractors, it employs an evidential
state classifier to estimate the confidence of the pedestrian states. Based on
extensive experiments conducted on twenty hours of real pedestrian data, we
demonstrate that ELESON outperforms significantly the state-of-the-art
approaches (where combined INS signals of both the conveyor state and human
actions are processed together), with 15% classification improvement in F1
score, stronger confidence discriminability with 10% increase in AUROC (Area
Under the Receiver Operating Characteristics), and low computational and memory
requirements on smartphones.
