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HOI-Loc: Towards unobstructive human localization with probabilistic multi-sensor fusion

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HOI-Loc : Towards unobstructive human localization with probabilistic multi-sensor fusion. / Ruan, Wenjie; Sheng, Quan Z.; Yao, Lina et al.

2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016. IEEE, 2016. p. 1-4 7457055.

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

Harvard

Ruan, W, Sheng, QZ, Yao, L, Yang, L & Gu, T 2016, HOI-Loc: Towards unobstructive human localization with probabilistic multi-sensor fusion. in 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016., 7457055, IEEE, pp. 1-4, 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016, Sydney, Australia, 14/03/16. https://doi.org/10.1109/PERCOMW.2016.7457055

APA

Ruan, W., Sheng, Q. Z., Yao, L., Yang, L., & Gu, T. (2016). HOI-Loc: Towards unobstructive human localization with probabilistic multi-sensor fusion. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 (pp. 1-4). [7457055] IEEE. https://doi.org/10.1109/PERCOMW.2016.7457055

Vancouver

Ruan W, Sheng QZ, Yao L, Yang L, Gu T. HOI-Loc: Towards unobstructive human localization with probabilistic multi-sensor fusion. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016. IEEE. 2016. p. 1-4. 7457055 doi: 10.1109/PERCOMW.2016.7457055

Author

Ruan, Wenjie ; Sheng, Quan Z. ; Yao, Lina et al. / HOI-Loc : Towards unobstructive human localization with probabilistic multi-sensor fusion. 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016. IEEE, 2016. pp. 1-4

Bibtex

@inproceedings{d1a550f602134c759e2dce0e83fcf317,
title = "HOI-Loc: Towards unobstructive human localization with probabilistic multi-sensor fusion",
abstract = "Unobtrusive indoor localization aims to localize people without requiring them to carry any devices or being actively involved with the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or domestic appliances, reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can reveal people's interleaved locations during daily living activities. Thus, this paper aims to enhance the performance of the RFID-based localization system by fusing human-object interactions. Specifically, we propose a general Bayesian probabilistic multi-sensor fusion framework to integrate both RSSI signals and human-object interaction events to infer the most likely location and trajectory. Unlike other RFID-based unobtrusive localization systems, which are limited to deployment and testing in cleared spacial areas, our system can work in a furnished environment. The extensive experiments with this system have a localization accuracy up to 96.7%, and average 0.58m tracking error.",
author = "Wenjie Ruan and Sheng, {Quan Z.} and Lina Yao and Lei Yang and Tao Gu",
year = "2016",
month = mar,
day = "14",
doi = "10.1109/PERCOMW.2016.7457055",
language = "English",
pages = "1--4",
booktitle = "2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016",
publisher = "IEEE",
note = "13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 ; Conference date: 14-03-2016 Through 18-03-2016",

}

RIS

TY - GEN

T1 - HOI-Loc

T2 - 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016

AU - Ruan, Wenjie

AU - Sheng, Quan Z.

AU - Yao, Lina

AU - Yang, Lei

AU - Gu, Tao

PY - 2016/3/14

Y1 - 2016/3/14

N2 - Unobtrusive indoor localization aims to localize people without requiring them to carry any devices or being actively involved with the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or domestic appliances, reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can reveal people's interleaved locations during daily living activities. Thus, this paper aims to enhance the performance of the RFID-based localization system by fusing human-object interactions. Specifically, we propose a general Bayesian probabilistic multi-sensor fusion framework to integrate both RSSI signals and human-object interaction events to infer the most likely location and trajectory. Unlike other RFID-based unobtrusive localization systems, which are limited to deployment and testing in cleared spacial areas, our system can work in a furnished environment. The extensive experiments with this system have a localization accuracy up to 96.7%, and average 0.58m tracking error.

AB - Unobtrusive indoor localization aims to localize people without requiring them to carry any devices or being actively involved with the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or domestic appliances, reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can reveal people's interleaved locations during daily living activities. Thus, this paper aims to enhance the performance of the RFID-based localization system by fusing human-object interactions. Specifically, we propose a general Bayesian probabilistic multi-sensor fusion framework to integrate both RSSI signals and human-object interaction events to infer the most likely location and trajectory. Unlike other RFID-based unobtrusive localization systems, which are limited to deployment and testing in cleared spacial areas, our system can work in a furnished environment. The extensive experiments with this system have a localization accuracy up to 96.7%, and average 0.58m tracking error.

U2 - 10.1109/PERCOMW.2016.7457055

DO - 10.1109/PERCOMW.2016.7457055

M3 - Conference contribution/Paper

AN - SCOPUS:84966549625

SP - 1

EP - 4

BT - 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016

PB - IEEE

Y2 - 14 March 2016 through 18 March 2016

ER -