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Device-free indoor localization and tracking through Human-Object Interactions

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Device-free indoor localization and tracking through Human-Object Interactions. / Ruan, Wenjie; Sheng, Quan Z.; Yao, Lina; Gu, Tao; Ruta, Michele; Shangguan, Longfei.

WoWMoM 2016 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks. IEEE, 2016. p. 1-9 7523524.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Ruan, W, Sheng, QZ, Yao, L, Gu, T, Ruta, M & Shangguan, L 2016, Device-free indoor localization and tracking through Human-Object Interactions. in WoWMoM 2016 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks., 7523524, IEEE, pp. 1-9, 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2016, Coimbra, Portugal, 21/06/16. https://doi.org/10.1109/WoWMoM.2016.7523524

APA

Ruan, W., Sheng, Q. Z., Yao, L., Gu, T., Ruta, M., & Shangguan, L. (2016). Device-free indoor localization and tracking through Human-Object Interactions. In WoWMoM 2016 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (pp. 1-9). [7523524] IEEE. https://doi.org/10.1109/WoWMoM.2016.7523524

Vancouver

Ruan W, Sheng QZ, Yao L, Gu T, Ruta M, Shangguan L. Device-free indoor localization and tracking through Human-Object Interactions. In WoWMoM 2016 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks. IEEE. 2016. p. 1-9. 7523524 https://doi.org/10.1109/WoWMoM.2016.7523524

Author

Ruan, Wenjie ; Sheng, Quan Z. ; Yao, Lina ; Gu, Tao ; Ruta, Michele ; Shangguan, Longfei. / Device-free indoor localization and tracking through Human-Object Interactions. WoWMoM 2016 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks. IEEE, 2016. pp. 1-9

Bibtex

@inproceedings{55804c20cd91422982d01eb3bcc96eb8,
title = "Device-free indoor localization and tracking through Human-Object Interactions",
abstract = "Device-free indoor localization aims to localize people without requiring them to carry any devices or being actively involved in the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a cluttered environment such as a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or metallic appliances, thus reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can potentially reveal people's interleaved locations during daily living activities, such as watching TV, opening the fridge door. This paper aims to enhance the performance of commercial off-the-shelf (COTS) RFID-based localization system by leveraging HOI contexts in a furnished home. Specifically, we propose a general Bayesian probabilistic framework to integrate both RSSI signals and HOI events to infer the most likely location and trajectory. Experiments conducted in a residential house demonstrate the effectiveness of our proposed method, in which we can localize a resident with average 95% accuracy and track a moving subject with 0.58m mean error distance.",
author = "Wenjie Ruan and Sheng, {Quan Z.} and Lina Yao and Tao Gu and Michele Ruta and Longfei Shangguan",
year = "2016",
month = jul,
day = "26",
doi = "10.1109/WoWMoM.2016.7523524",
language = "English",
pages = "1--9",
booktitle = "WoWMoM 2016 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks",
publisher = "IEEE",
note = "17th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2016 ; Conference date: 21-06-2016 Through 24-06-2016",

}

RIS

TY - GEN

T1 - Device-free indoor localization and tracking through Human-Object Interactions

AU - Ruan, Wenjie

AU - Sheng, Quan Z.

AU - Yao, Lina

AU - Gu, Tao

AU - Ruta, Michele

AU - Shangguan, Longfei

PY - 2016/7/26

Y1 - 2016/7/26

N2 - Device-free indoor localization aims to localize people without requiring them to carry any devices or being actively involved in the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a cluttered environment such as a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or metallic appliances, thus reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can potentially reveal people's interleaved locations during daily living activities, such as watching TV, opening the fridge door. This paper aims to enhance the performance of commercial off-the-shelf (COTS) RFID-based localization system by leveraging HOI contexts in a furnished home. Specifically, we propose a general Bayesian probabilistic framework to integrate both RSSI signals and HOI events to infer the most likely location and trajectory. Experiments conducted in a residential house demonstrate the effectiveness of our proposed method, in which we can localize a resident with average 95% accuracy and track a moving subject with 0.58m mean error distance.

AB - Device-free indoor localization aims to localize people without requiring them to carry any devices or being actively involved in the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a cluttered environment such as a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or metallic appliances, thus reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can potentially reveal people's interleaved locations during daily living activities, such as watching TV, opening the fridge door. This paper aims to enhance the performance of commercial off-the-shelf (COTS) RFID-based localization system by leveraging HOI contexts in a furnished home. Specifically, we propose a general Bayesian probabilistic framework to integrate both RSSI signals and HOI events to infer the most likely location and trajectory. Experiments conducted in a residential house demonstrate the effectiveness of our proposed method, in which we can localize a resident with average 95% accuracy and track a moving subject with 0.58m mean error distance.

U2 - 10.1109/WoWMoM.2016.7523524

DO - 10.1109/WoWMoM.2016.7523524

M3 - Conference contribution/Paper

AN - SCOPUS:84983770560

SP - 1

EP - 9

BT - WoWMoM 2016 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks

PB - IEEE

T2 - 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2016

Y2 - 21 June 2016 through 24 June 2016

ER -