Scipediacontent: Scipediacontent moved page Draft Content 485333069 to Kleisarchaki et al 2018a

2021-02-02T00:55:34Z

Scipediacontent moved page Draft Content 485333069 to Kleisarchaki et al 2018a

Scipediacontent: Created page with " == Abstract == International audience; Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The prolifer..."

2021-02-02T00:55:29Z

Created page with " == Abstract == International audience; Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The prolifer..."

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== Abstract ==

International audience; Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., $CO_2$) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.

== Original document ==

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

* [https://hal-cea.archives-ouvertes.fr/cea-02313771/file/08449765.pdf https://hal-cea.archives-ouvertes.fr/cea-02313771/file/08449765.pdf]

* [http://xplorestaging.ieee.org/ielx7/8412803/8449730/08449765.pdf?arnumber=8449765 http://xplorestaging.ieee.org/ielx7/8412803/8449730/08449765.pdf?arnumber=8449765],
: [http://dx.doi.org/10.1109/wowmom.2018.8449765 http://dx.doi.org/10.1109/wowmom.2018.8449765]

* [https://dblp.uni-trier.de/db/conf/wowmom/wowmom2018.html#YoshidaKGN18 https://dblp.uni-trier.de/db/conf/wowmom/wowmom2018.html#YoshidaKGN18],
: [https://ieeexplore.ieee.org/document/8449765 https://ieeexplore.ieee.org/document/8449765],
: [https://keio.pure.elsevier.com/ja/publications/indoor-occupancy-estimation-via-location-aware-hmm-an-iot-approac https://keio.pure.elsevier.com/ja/publications/indoor-occupancy-estimation-via-location-aware-hmm-an-iot-approac],
: [https://academic.microsoft.com/#/detail/2888879444 https://academic.microsoft.com/#/detail/2888879444]

* [https://hal-cea.archives-ouvertes.fr/cea-02313771 https://hal-cea.archives-ouvertes.fr/cea-02313771],
: [https://hal-cea.archives-ouvertes.fr/cea-02313771/document https://hal-cea.archives-ouvertes.fr/cea-02313771/document],
: [https://hal-cea.archives-ouvertes.fr/cea-02313771/file/08449765.pdf https://hal-cea.archives-ouvertes.fr/cea-02313771/file/08449765.pdf]

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Kleisarchaki et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 485333069 to Kleisarchaki et al 2018a

Scipediacontent: Created page with " == Abstract == International audience; Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The prolifer..."