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Elevator, Escalator or Neither? Classifying Pedestrian Conveyor State Using Inertial Navigation System

Knowing a pedestrian's conveyor state of "elevator," "escalator," or "neither" is fundamental in many applications such as indoor navigation and people flow management. We study, for the first time, classifying the conveyor state of a pedestrian, given the multimodal INS (inertial navigation system) readings of accelerometer, gyroscope and magnetometer sampled from the pedestrian phone. This problem is challenging because the INS signals of the conveyor state are entangled with unpredictable independent pedestrian motions, confusing the classification 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 causal feature extractor to disentangle the conveyor state from pedestrian motion, and a magnetic feature extractor to capture the unique magnetic characteristics of moving elevators and escalators. Given the results of the extractors, it then employs an evidential state classifier to estimate the confidence of the conveyor states. Based on extensive experiments conducted on real pedestrian data, we demonstrate that ELESON outperforms significantly previous INS-based classification approaches, achieving 14% improvement in F1 score, strong confidence discriminability of 0.81 in AUROC (Area Under the Receiver Operating Characteristics), and low computational and memory requirements for smartphone deployment.

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