IndoLabel: Predicting Indoor Location Class by Discovering Location-Specific Sensor Data Motifs
IndoLabel: Predicting Indoor Location Class by Discovering Location-Specific Sensor Data Motifs
复制标题
DOI：
10.1109/jsen.2021.3102916
复制
发表时间：
2022-03-15
影响因子：
4.3
通讯作者：
Kawanabe, Motoaki
中科院分区：
文献类型：
--
作者：
Dissanayake, Thilina;Maekawa, Takuya;Kawanabe, Motoaki
本研究提出了一种预测房间（如厨房和洗手间）位置类别的方法，通过在用户的传感器设备（如智能手表）观察到的传感器数据中发现特定于位置的传感器数据主题，而不需要在目标环境中收集标记的训练数据。例如，我们可以在厨房中使用人体佩戴的加速度计观察到厨刀切割动作对应的类似波形，也可以在浴室中通过主动声音探测观察到类似的声音特征，因为浴室有防水墙。这表明，在几乎所有环境中，这种位置特定的传感器数据基序都可以作为位置类别预测的固有信息。本研究提出了一种新的方法，通过计算时间序列中每个基序的“位置特异性”分数，从时间序列传感器数据中自动检测位置特异性基序。以往关于位置类别预测的研究假设特定位置传感器数据总是在房间中观察到，或者使用手工制作的规则和模板来检测特定位置传感器数据，导致难以将其应用于几个现实环境。相反，我们名为IndoLabel的方法可以自动发现特定于位置类的短传感器数据主题，并且可以自动构建与环境无关的位置分类器，而不需要手工制作规则和模板。在真实的室内环境中，使用leave-one-environment-out交叉验证对所提出的方法进行了评估，尽管无法获得目标环境中的标记训练数据，但仍取得了最先进的性能。
This study presents a method for predicting location classes of a room such as a kitchen, and restroom, where a user is located by discovering location-specific sensor data motifs in sensor data observed by user's sensor devices, such as smartwatch, without requiring labeled training data collected in a target environment. For example, we can observe similar waveforms corresponding to kitchen knife chopping actions using body-worn accelerometers in kitchens and can also observe similar sound features by active sound probing in bathrooms because of their water-resistant walls. This indicates that such location-specific sensor data motifs can be inherent information for location class prediction in almost every environment. This study proposes a novel method that automatically detects location-specific motifs from time series sensor data by calculating a score that represents the "location specificity" of each motif in a time series. Previous studies on location class prediction assume that location-specific sensor data are always observed in a room or use handcrafted rules and templates to detect location-specific sensor data resulting in difficulties in applying them to several realistic environments. In contrast, our method, named IndoLabel, can automatically discover short sensor data motifs, specific to a location class, and can automatically build an environment-independent location classifier without requiring handcrafted rules and templates. The proposed method was evaluated in real house environments using leave-one-environment-out cross-validation and achieved a state-of-the-art performance although labeled training data in the target environment was unavailable.