DSpace@MIT (Massachusetts Institute of Technology) · 2014 · 61 citations · 18 references
Open access
ComputingInformation TechnologyMobile ComputingSensorsWireless CommunicationsGesture RecognitionMobile Human-computer InteractionMobile SensingMotion CaptureMotion SensorsTracking SystemsAccuracyBody Gesture RecognitionGesture SensingGesture-based InterfacesMotion TrackingSensor-based Gesture RecognitionWi-fi SensingWireless Body Sensor Networks
Abstract – Recently, we have witnessed the emergence of technologies that can localize a user and track her gestures based purely on radio reflections off the person’s body. These technologies work even if the user is behind a wall or ob-struction. However, for these technologies to be fully prac-tical, they need to address major challenges such as scaling to multiple people, accurately localizing them and tracking their gestures, and localizing static users as opposed to re-quiring the user to move to be detectable. This paper presents WiZ, the first multi-person centimeter-scale motion tracking system that pinpoints people’s locations based purely on RF reflections off their bodies. WiZ can also locate static users by sensing minute changes in their RF reflections due to breathing. Further, it can track concurrent gestures made by different individuals, even when they carry no wireless device on them. We implement a prototype of WiZ and show that it can localize up to five users each with a median accuracy of 8-18 cm and 7-11 cm in the x and y dimensions respec-tively. WiZ can also detect 3D pointing gestures of multiple users with a median orientation error of 8 − 16 ◦ for each of them. Finally, WiZ can track breathing motion and output the breath count of multiple people with high accuracy. 1.
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Whole-home gesture recognition using wireless signals
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