Chair for Information Science, University Regensburg, 93053 Regensburg, Germany.
Sensors (Basel). 2021 May 7;21(9):3243. doi: 10.3390/s21093243.
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.
在基于智能手机的行人导航系统中，详细了解用户活动和设备放置位置是关键信息。地标（如楼梯或电梯）可以帮助系统确定用户在建筑物内的位置，并且可以根据当前上下文调整导航指令，以提供更有意义的帮助。通常，大多数人体活动识别（HAR）方法区分一般活动，例如行走、站立或坐着。在这项工作中，我们研究了更具体的活动，这些活动针对行人导航的用例进行了定制，包括不同类型的静止和运动行为。我们首先收集了一个包含 28 种设备放置和活动组合的数据集，总数据来自三个传感器，超过 6 小时。然后，我们使用基于 LSTM 的机器学习（ML）方法成功训练了层次分类器，可以区分这些设备放置和活动。测试结果表明，设备放置分类的准确率（97.2%）与该数据集的最新基准相当，而在移动设备上的资源消耗较少。活动识别性能高度依赖于分类任务，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层次分类器应用于实验和自然主义数据集，以分析典型导航会话过程中的活动模式，并研究用户活动和设备放置之间的相关性，从而深入了解真实世界的导航行为。
