  Human-machine systems required a deep understanding of human behaviors. Most
existing research on action recognition has focused on discriminating between
different actions, however, the quality of executing an action has received
little attention thus far. In this paper, we study the quality assessment of
driving behaviors and present WiQ, a system to assess the quality of actions
based on radio signals. This system includes three key components, a deep
neural network based learning engine to extract the quality information from
the changes of signal strength, a gradient based method to detect the signal
boundary for an individual action, and an activitybased fusion policy to
improve the recognition performance in a noisy environment. By using the
quality information, WiQ can differentiate a triple body status with an
accuracy of 97%, while for identification among 15 drivers, the average
accuracy is 88%. Our results show that, via dedicated analysis of radio
signals, a fine-grained action characterization can be achieved, which can
facilitate a large variety of applications, such as smart driving assistants.
