Abstract

International audience; 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%.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1109/iecon.2019.8927472
https://keio.pure.elsevier.com/en/publications/exploring-variability-in-iot-data-for-human-activity-recognition,
https://academic.microsoft.com/#/detail/2995796420
https://hal-cea.archives-ouvertes.fr/cea-02313761/document,
https://hal-cea.archives-ouvertes.fr/cea-02313761/file/IECON_sakuma_edit_format.pdf
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1109/iecon.2019.8927472
Licence: CC BY-NC-SA license

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