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Recently several ground-breaking RF-based motion recognition systems wereproposed to detect and/or recognize macro/micro human movements. These systemsoften suffer from various interferences caused by multiple-users movingsimultaneously, resulting in extremely low recognition accuracy. To tackle thischallenge, we propose a novel system, called Motion-Fi, which marries batteryfree wireless backscattering and device-free sensing. Motion-Fi is an accurate,interference tolerable motion-recognition system, which counts repetitivemotions without using scenario-dependent templates or profiles and enablesmulti-users performing certain motions simultaneously because of the relativelyshort transmission range of backscattered signals. Although the repetitivemotions are fairly well detectable through the backscattering signals intheory, in reality they get blended into various other system noises during themotion. Moreover, irregular motion patterns among users will lead to expensivecomputation cost for motion recognition. We build a backscattering wirelessplatform to validate our design in various scenarios for over 6 months whendifferent persons, distances and orientations are incorporated. In ourexperiments, the periodicity in motions could be recognized without anylearning or training process, and the accuracy of counting such motions can beachieved within 5% count error. With little efforts in learning the patterns,our method could achieve 93.1% motion-recognition accuracy for a variety ofmotions. Moreover, by leveraging the periodicity of motions, the recognitionaccuracy could be further improved to nearly 100% with only 3 repetitions. Ourexperiments also show that the motions of multiple persons separating by around2 meters cause little accuracy reduction in the counting process.

From Communication to Sensing : Recognizing and Counting Repetitive Motions with Wireless Backscattering