In smartphone-based pedestrian navigation systems, detailed knowledge about user activity and device placement is a key information. Landmarks such as staircases or elevators can help the system in determining the user position when located inside buildings, and navigation instructions can be adapted to the current context in order to provide more meaningful assistance. Typically, most human activity recognition (HAR) approaches distinguish between general activities such as walking, standing or sitting. In this work, we investigate more specific activities that are tailored towards the use-case of pedestrian navigation, including different kinds of stationary and locomotion behavior. We first collect a dataset of 28 combinations of device placements and activities, in total consisting of over 6 h of data from three sensors. We then use LSTM-based machine learning (ML) methods to successfully train hierarchical classifiers that can distinguish between these placements and activities. Test results show that the accuracy of device placement classification (97.2%) is on par with a state-of-the-art benchmark in this dataset while being less resource-intensive on mobile devices. Activity recognition performance highly depends on the classification task and ranges from 62.6% to 98.7%, once again performing close to the benchmark. Finally, we demonstrate in a case study how to apply the hierarchical classifiers to experimental and naturalistic datasets in order to analyze activity patterns during the course of a typical navigation session and to investigate the correlation between user activity and device placement, thereby gaining insights into real-world navigation behavior.

中文翻译：

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行人导航环境中基于martphone的活动识别

在基于智能手机的行人导航系统中，有关用户活动和设备放置的详细知识是关键信息。诸如楼梯或电梯之类的地标可以帮助系统确定位于建筑物内部时的用户位置，并且导航指令可以适应当前环境，以便提供更有意义的帮助。通常，大多数人类活动识别（HAR）方法会区分一般活动，例如步行，站立或坐下。在这项工作中，我们将针对行人导航的用例进行量身定制更具体的活动，包括不同类型的固定和运动行为。我们首先收集28个设备放置和活动组合的数据集，总共包括来自三个传感器的6小时以上的数据。然后，我们使用基于LSTM的机器学习（ML）方法来成功训练可以区分这些展示位置和活动的分层分类器。测试结果表明，设备放置分类的准确性（97.2％）与该数据集中的最新基准相当，同时在移动设备上的资源占用较少。活动识别性能很大程度上取决于分类任务，范围从62.6％到98.7％，再次接近基准。最后，我们在一个案例研究中演示了如何将分层分类器应用于实验数据集和自然主义数据集，以便分析典型导航会话过程中的活动模式，并调查用户活动与设备放置之间的相关性，