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Exploring Variability in IoT Data for Human Activity Recognition


Human Activity Recognition (HAR) is a well-studied scientific area that has gained much traction with the rise of Internet of Things (IoT). Despite the interest in HAR for a wide spectrum of domains (technological, medical, etc.) only a few works exist, which study the variability in IoT data. To correctly perceive this variability, it is essential to dynamically model the evolving context of daily-life activities. Additionally, it is required to reduce the calculation cost of HAR, which is crucial for security and real-time applications. For the purpose of dynamically modeling, three context-aware approaches are formalized along with a context-free baseline. This study demonstrates improvements in terms of both of accuracy and calculation cost by considering variability in IoT data; our experimental study on real datasets reduced calculation cost by 20% while increasing accuracy by 20%.
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cea-02313761 , version 1 (11-10-2019)


  • HAL Id : cea-02313761 , version 1


Yuiko Sakuma, Sofia Kleisarchaki, Hiroaki Nishi, Levent Gurgen. Exploring Variability in IoT Data for Human Activity Recognition. IECON'19, Oct 2019, Lisbon, Portugal. ⟨cea-02313761⟩
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