Scipediacontent: Scipediacontent moved page Draft Content 390621603 to Sakuma et al 2019a

2021-02-02T04:33:54Z

Scipediacontent moved page Draft Content 390621603 to Sakuma et al 2019a

Scipediacontent: Created page with " == 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 (..."

2021-02-02T04:33:48Z

Created page with " == 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 (..."

New page

== 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:

* [https://hal-cea.archives-ouvertes.fr/cea-02313761/file/IECON_sakuma_edit_format.pdf https://hal-cea.archives-ouvertes.fr/cea-02313761/file/IECON_sakuma_edit_format.pdf]

* [http://xplorestaging.ieee.org/ielx7/8897531/8926608/08927472.pdf?arnumber=8927472 http://xplorestaging.ieee.org/ielx7/8897531/8926608/08927472.pdf?arnumber=8927472],
: [http://dx.doi.org/10.1109/iecon.2019.8927472 http://dx.doi.org/10.1109/iecon.2019.8927472]

* [https://dblp.uni-trier.de/db/conf/iecon/iecon2019.html#SakumaKGN19 https://dblp.uni-trier.de/db/conf/iecon/iecon2019.html#SakumaKGN19],
: [https://keio.pure.elsevier.com/en/publications/exploring-variability-in-iot-data-for-human-activity-recognition https://keio.pure.elsevier.com/en/publications/exploring-variability-in-iot-data-for-human-activity-recognition],
: [https://academic.microsoft.com/#/detail/2995796420 https://academic.microsoft.com/#/detail/2995796420]

* [https://hal-cea.archives-ouvertes.fr/cea-02313761 https://hal-cea.archives-ouvertes.fr/cea-02313761],
: [https://hal-cea.archives-ouvertes.fr/cea-02313761/document https://hal-cea.archives-ouvertes.fr/cea-02313761/document],
: [https://hal-cea.archives-ouvertes.fr/cea-02313761/file/IECON_sakuma_edit_format.pdf https://hal-cea.archives-ouvertes.fr/cea-02313761/file/IECON_sakuma_edit_format.pdf]

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Sakuma et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 390621603 to Sakuma et al 2019a

Scipediacontent: Created page with " == 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 (..."