MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices. Gu, Y., Zhan, J., Ji, Y., Li, J., Ren, F., & Gao, S. IEEE Internet of Things Journal, 4(6):2326-2341, IEEE Press, 12, 2017. ![MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.
@article{
title = {MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices},
type = {article},
year = {2017},
identifiers = {[object Object]},
keywords = {channel-state-information,human-presence-monitoring,motion-detection},
pages = {2326-2341},
volume = {4},
websites = {http://dx.doi.org/10.1109/jiot.2017.2754578},
month = {12},
publisher = {IEEE Press},
id = {9b9f64c5-cef7-3ac3-8158-d6aec7e26a57},
created = {2018-07-12T21:30:54.670Z},
file_attached = {false},
profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
last_modified = {2018-07-12T21:30:54.670Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {gu:humanMotionSensing17},
source_type = {article},
private_publication = {false},
abstract = {Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.},
bibtype = {article},
author = {Gu, Yu and Zhan, Jinhai and Ji, Yusheng and Li, Jie and Ren, Fuji and Gao, Shangbing},
journal = {IEEE Internet of Things Journal},
number = {6}
}
Downloads: 0
{"_id":"WNTRuhdAeeTsYFJ25","bibbaseid":"gu-zhan-ji-li-ren-gao-mosenseanrfbasedmotiondetectionsystemviaofftheshelfwifidevices-2017","downloads":0,"creationDate":"2019-02-15T15:14:57.614Z","title":"MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices","author_short":["Gu, Y.","Zhan, J.","Ji, Y.","Li, J.","Ren, F.","Gao, S."],"year":2017,"bibtype":"article","biburl":null,"bibdata":{"title":"MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices","type":"article","year":"2017","identifiers":"[object Object]","keywords":"channel-state-information,human-presence-monitoring,motion-detection","pages":"2326-2341","volume":"4","websites":"http://dx.doi.org/10.1109/jiot.2017.2754578","month":"12","publisher":"IEEE Press","id":"9b9f64c5-cef7-3ac3-8158-d6aec7e26a57","created":"2018-07-12T21:30:54.670Z","file_attached":false,"profile_id":"f954d000-ce94-3da6-bd26-b983145a920f","group_id":"b0b145a3-980e-3ad7-a16f-c93918c606ed","last_modified":"2018-07-12T21:30:54.670Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"gu:humanMotionSensing17","source_type":"article","private_publication":false,"abstract":"Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.","bibtype":"article","author":"Gu, Yu and Zhan, Jinhai and Ji, Yusheng and Li, Jie and Ren, Fuji and Gao, Shangbing","journal":"IEEE Internet of Things Journal","number":"6","bibtex":"@article{\n title = {MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices},\n type = {article},\n year = {2017},\n identifiers = {[object Object]},\n keywords = {channel-state-information,human-presence-monitoring,motion-detection},\n pages = {2326-2341},\n volume = {4},\n websites = {http://dx.doi.org/10.1109/jiot.2017.2754578},\n month = {12},\n publisher = {IEEE Press},\n id = {9b9f64c5-cef7-3ac3-8158-d6aec7e26a57},\n created = {2018-07-12T21:30:54.670Z},\n file_attached = {false},\n profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},\n group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},\n last_modified = {2018-07-12T21:30:54.670Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {gu:humanMotionSensing17},\n source_type = {article},\n private_publication = {false},\n abstract = {Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.},\n bibtype = {article},\n author = {Gu, Yu and Zhan, Jinhai and Ji, Yusheng and Li, Jie and Ren, Fuji and Gao, Shangbing},\n journal = {IEEE Internet of Things Journal},\n number = {6}\n}","author_short":["Gu, Y.","Zhan, J.","Ji, Y.","Li, J.","Ren, F.","Gao, S."],"urls":{"Website":"http://dx.doi.org/10.1109/jiot.2017.2754578"},"bibbaseid":"gu-zhan-ji-li-ren-gao-mosenseanrfbasedmotiondetectionsystemviaofftheshelfwifidevices-2017","role":"author","keyword":["channel-state-information","human-presence-monitoring","motion-detection"],"downloads":0},"search_terms":["mosense","based","motion","detection","system","via","shelf","wifi","devices","gu","zhan","ji","li","ren","gao"],"keywords":["channel-state-information","human-presence-monitoring","motion-detection"],"authorIDs":[]}