  Due to the severe multipath effect, no satisfactory device-free methods have
ever been found for indoor speed estimation problem, especially in
non-line-of-sight scenarios, where the direct path between the source and
observer is blocked. In this paper, we present WiSpeed, a universal
low-complexity indoor speed estimation system leveraging radio signals, such as
commercial WiFi, LTE, 5G, etc., which can work in both device-free and
device-based situations. By exploiting the statistical theory of
electromagnetic waves, we establish a link between the autocorrelation function
of the physical layer channel state information and the speed of a moving
object, which lays the foundation of WiSpeed. WiSpeed differs from the other
schemes requiring strong line-of-sight conditions between the source and
observer in that it embraces the rich-scattering environment typical for
indoors to facilitate highly accurate speed estimation. Moreover, as a
calibration-free system, WiSpeed saves the users' efforts from large-scale
training and fine-tuning of system parameters. In addition, WiSpeed could
extract the stride length as well as detect abnormal activities such as falling
down, a major threat to seniors that leads to a large number of fatalities
every year. Extensive experiments show that WiSpeed achieves a mean absolute
percentage error of 4.85% for device-free human walking speed estimation and
4.62% for device-based speed estimation, and a detection rate of 95% without
false alarms for fall detection.
