Exploring Variability in IoT Data for Human Activity Recognition

Yuiko Sakuma (1) , Sofia Kleisarchaki (2) , Hiroaki Nishi (3) , Levent Gurgen (2)

1 Keio University [Tokyo]
2 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
3 Faculty of Science and Technology (Keio University)

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%.

Mots clés: Spatio-Temporal Context; Internet of Things; Variability; Human Activity Recognition

Domaines: Informatique [cs]; Apprentissage [cs.LG]

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⟩