IndoLabel: Predicting Indoor Location Class by Discovering Location-Specific Sensor Data Motifs
| Autor: | Taiki Miyanishi, Takuya Maekawa, Thilina Dissanayake, Motoaki Kawanabe, Takahiro Hara |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Sensors Journal. 22:5372-5385 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2021.3102916 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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