  In this paper we focus on the problem of human activity recognition without
identification of the individuals in a scene. We consider using Wi-Fi signals
to detect certain human mobility behaviors such as stationary, walking, or
running. The main objective is to successfully detect these behaviors for the
individuals and based on that enable detection of the crowd's overall mobility
behavior. We propose a method which infers mobility behaviors in two stages:
from Wi-Fi signals to trajectories and from trajectories to the mobility
behaviors. We evaluate the applicability of the proposed approach using the
StudentLife dataset which contains Wi-Fi, GPS, and accelerometer measurements
collected from smartphones of 49 students within a three-month period. The
experimental results indicate that there is high correlation between stability
of Wi-Fi signals and mobility activity. This unique characteristic provides
sufficient evidences to extend the proposed idea to mobility analytics of
groups of people in the future.
