Fundamentals of wireless communication
Fundamentals of wireless communication
Towards radar-enabled sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Proceedings of the 15th ACM conference on Computer and communications security
Experimental results with two wireless power transfer systems
RWS'09 Proceedings of the 4th international conference on Radio and wireless symposium
SNUPI: sensor nodes utilizing powerline infrastructure
Proceedings of the 12th ACM international conference on Ubiquitous computing
Humantenna: using the body as an antenna for real-time whole-body interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An ultra-low-power human body motion sensor using static electric field sensing
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Whole-home gesture recognition using wireless signals
Proceedings of the 19th annual international conference on Mobile computing & networking
RFID shakables: pairing radio-frequency identification tags with the help of gesture recognition
Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
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Existing gesture-recognition systems consume significant power and computational resources that limit how they may be used in low-end devices. We introduce AllSee, the first gesture-recognition system that can operate on a range of computing devices including those with no batteries. AllSee consumes three to four orders of magnitude lower power than state-of-the-art systems and can enable always-on gesture recognition for smartphones and tablets. It extracts gesture information from existing wireless signals (e.g., TV transmissions), but does not incur the power and computational overheads of prior wireless approaches. We build AllSee prototypes that can recognize gestures on RFID tags and power-harvesting sensors. We also integrate our hardware with an off-the-shelf Nexus S phone and demonstrate gesture recognition in through-the-pocket scenarios. Our results show that AllSee achieves classification accuracies as high as 97% over a set of eight gestures.