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.

翻译：将行人分类为“电梯”、“自动扶梯”或“两者皆非”三种传送装置状态之一，是室内定位和人群流动分析等众多应用的基础。我们首次基于手机采样的加速度计、陀螺仪和磁力计的惯性导航系统（INS）读数，对行人传送装置状态进行估计。该问题具有挑战性，因为传送装置状态的INS信号与不可预测的任意人体动作耦合且受其干扰，从而混淆了决策过程。我们提出ELESON，一种新颖、高效且轻量级的基于INS的深度学习方法，用于分类行人处于电梯、自动扶梯或两者皆非的状态。ELESON利用运动特征提取器在特征空间中将传送装置状态与人体动作解耦，并采用磁特征提取器来区分电梯与自动扶梯的速度差异。基于提取器的结果，它使用证据状态分类器估计行人状态的可信度。通过对二十小时真实行人数据的大量实验，我们证明ELESON显著优于现有最先进方法（后者将传送装置状态和人体动作的INS信号联合处理），F1分数分类性能提升15%，AUROC（受试者工作特征曲线下面积）的置信度区分能力增强10%，且在智能手机上计算和内存需求较低。