The ability to infer the motion of the user has previously been possible only with the usage of additional hardware. In this paper we show how motion sensing can be obtained just by observing the WLAN radio’s signal strength and its fluctuations. For the first time, we have analyzed the signal strength fluctuations in three different domains: (i) time, (ii) frequency, and (iii) space. Our analysis in all these three domains confirmed our claim that ”when a device is moving, signal strengths from all heard access points vary much greater and more obvious compared to when a device is still”. Using this observation, we present two algorithms Frequency-Spread Motion Detection and Spatially-Spread Motion Detection to infer the motion of the user. A two-state classification scheme is used in both algorithms to deduce the user motion as either ’still’ or ’moving’. The results and performances are benchmarked against the ground truth using a machine learning toolkit. Both these algorithms show an overall classification accuracy of 95% and 97% respectively, and use only the radio signal that the wireless device should have to perform its normal operation. Finally we discuss how adding motion status inferred from our motion detection algorithms at runtime improves accuracy of our calibration-free localization algorithm to <5m.