Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/129379
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYongpan Zouen_US
dc.contributor.authorJiang Xiaoen_US
dc.contributor.authorJinsong Hanen_US
dc.contributor.authorKaishun Wuen_US
dc.contributor.authorYun Lien_US
dc.contributor.authorLionel M. Nien_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:00:42Z-
dc.date.available2020-04-06T07:00:42Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMC.2016.2549518en_US
dc.identifier.urihttp://localhost/handle/Hannan/129379-
dc.description.abstractGesture recognition has emerged recently as a promising application in our daily lives. Owing to low cost, prevalent availability, and structural simplicity, RFID shall become a popular technology for gesture recognition. However, the performance of existing RFID-based gesture recognition systems is constrained by unfavorable intrusiveness to users, requiring users to attach tags on their bodies. To overcome this, we propose GRfid, a novel device-free gesture recognition system based on phase information output by COTS RFID devices. Our work stems from the key insight that the RFID phase information is capable of capturing the spatial features of various gestures with low-cost commodity hardware. In GRfid, after data are collected by hardware, we process the data by a sequence of functional blocks, namely data preprocessing, gesture detection, profiles training, and gesture recognition, all of which are well-designed to achieve high performance in gesture recognition. We have implemented GRfid with a commercial RFID reader and multiple tags, and conducted extensive experiments in different scenarios to evaluate its performance. The results demonstrate that GRfid can achieve an average recognition accuracy of 96.5 and 92.8 percent in the identical-position and diverse-positions scenario, respectively. Moreover, experiment results show that GRfid is robust against environmental interference and tag orientations.en_US
dc.format.extent381,en_US
dc.format.extent393en_US
dc.publisherIEEEen_US
dc.relation.haspart7445209.pdfen_US
dc.titleGRfid: A Device-Free RFID-Based Gesture Recognition Systemen_US
dc.typeArticleen_US
dc.journal.volume16en_US
dc.journal.issue2en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7445209.pdf1.36 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYongpan Zouen_US
dc.contributor.authorJiang Xiaoen_US
dc.contributor.authorJinsong Hanen_US
dc.contributor.authorKaishun Wuen_US
dc.contributor.authorYun Lien_US
dc.contributor.authorLionel M. Nien_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:00:42Z-
dc.date.available2020-04-06T07:00:42Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMC.2016.2549518en_US
dc.identifier.urihttp://localhost/handle/Hannan/129379-
dc.description.abstractGesture recognition has emerged recently as a promising application in our daily lives. Owing to low cost, prevalent availability, and structural simplicity, RFID shall become a popular technology for gesture recognition. However, the performance of existing RFID-based gesture recognition systems is constrained by unfavorable intrusiveness to users, requiring users to attach tags on their bodies. To overcome this, we propose GRfid, a novel device-free gesture recognition system based on phase information output by COTS RFID devices. Our work stems from the key insight that the RFID phase information is capable of capturing the spatial features of various gestures with low-cost commodity hardware. In GRfid, after data are collected by hardware, we process the data by a sequence of functional blocks, namely data preprocessing, gesture detection, profiles training, and gesture recognition, all of which are well-designed to achieve high performance in gesture recognition. We have implemented GRfid with a commercial RFID reader and multiple tags, and conducted extensive experiments in different scenarios to evaluate its performance. The results demonstrate that GRfid can achieve an average recognition accuracy of 96.5 and 92.8 percent in the identical-position and diverse-positions scenario, respectively. Moreover, experiment results show that GRfid is robust against environmental interference and tag orientations.en_US
dc.format.extent381,en_US
dc.format.extent393en_US
dc.publisherIEEEen_US
dc.relation.haspart7445209.pdfen_US
dc.titleGRfid: A Device-Free RFID-Based Gesture Recognition Systemen_US
dc.typeArticleen_US
dc.journal.volume16en_US
dc.journal.issue2en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7445209.pdf1.36 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYongpan Zouen_US
dc.contributor.authorJiang Xiaoen_US
dc.contributor.authorJinsong Hanen_US
dc.contributor.authorKaishun Wuen_US
dc.contributor.authorYun Lien_US
dc.contributor.authorLionel M. Nien_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:00:42Z-
dc.date.available2020-04-06T07:00:42Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMC.2016.2549518en_US
dc.identifier.urihttp://localhost/handle/Hannan/129379-
dc.description.abstractGesture recognition has emerged recently as a promising application in our daily lives. Owing to low cost, prevalent availability, and structural simplicity, RFID shall become a popular technology for gesture recognition. However, the performance of existing RFID-based gesture recognition systems is constrained by unfavorable intrusiveness to users, requiring users to attach tags on their bodies. To overcome this, we propose GRfid, a novel device-free gesture recognition system based on phase information output by COTS RFID devices. Our work stems from the key insight that the RFID phase information is capable of capturing the spatial features of various gestures with low-cost commodity hardware. In GRfid, after data are collected by hardware, we process the data by a sequence of functional blocks, namely data preprocessing, gesture detection, profiles training, and gesture recognition, all of which are well-designed to achieve high performance in gesture recognition. We have implemented GRfid with a commercial RFID reader and multiple tags, and conducted extensive experiments in different scenarios to evaluate its performance. The results demonstrate that GRfid can achieve an average recognition accuracy of 96.5 and 92.8 percent in the identical-position and diverse-positions scenario, respectively. Moreover, experiment results show that GRfid is robust against environmental interference and tag orientations.en_US
dc.format.extent381,en_US
dc.format.extent393en_US
dc.publisherIEEEen_US
dc.relation.haspart7445209.pdfen_US
dc.titleGRfid: A Device-Free RFID-Based Gesture Recognition Systemen_US
dc.typeArticleen_US
dc.journal.volume16en_US
dc.journal.issue2en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7445209.pdf1.36 MBAdobe PDF