Bibtex Journal

IndoLabel: Predicting Indoor Location Class by Discovering Location-specific Sensor Data Motifs

Authors Thilina Dissanayake Takuya Maekawa Takahiro Hara Taiki Miyanishi Motoaki Kawanabe
Book IEEE Sensors Journal
Published 2021 . 08
Fund 10.13039/501100001691-Japan Society for the Promotion of Science (Grant Number: JP16H06539 and JP17H04679) 10.13039/501100003382-Core Research for Evolutional Science and Technology (Grant Number: JPMJCR15E2)
DOI 10.1109/JSEN.2021.3102916
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.
  title     = {IndoLabel: Predicting Indoor Location Class by Discovering Location-specific Sensor Data Motifs},
  author    = {Thilina Dissanayake and Maekawa, Takuya and Hara, Takahiro and Miyanishi, Taiki and Kawanabe, Motoaki},
  journal   = {IEEE Sensors Journal},
  month     = {8},
  year      = {2021},
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