Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -