  Human activity recognition has gained importance in recent years due to its
applications in various fields such as health, security and surveillance,
entertainment, and intelligent environments. A significant amount of work has
been done on human activity recognition and researchers have leveraged
different approaches, such as wearable, object-tagged, and device-free, to
recognize human activities. In this article, we present a comprehensive survey
of the work conducted over the period 2010-2018 in various areas of human
activity recognition with main focus on device-free solutions. The device-free
approach is becoming very popular due to the fact that the subject is not
required to carry anything, instead, the environment is tagged with devices to
capture the required information. We propose a new taxonomy for categorizing
the research work conducted in the field of activity recognition and divide the
existing literature into three sub-areas: action-based, motion-based, and
interaction-based. We further divide these areas into ten different sub-topics
and present the latest research work in these sub-topics. Unlike previous
surveys which focus only on one type of activities, to the best of our
knowledge, we cover all the sub-areas in activity recognition and provide a
comparison of the latest research work in these sub-areas. Specifically, we
discuss the key attributes and design approaches for the work presented. Then
we provide extensive analysis based on 10 important metrics, to give the
reader, a complete overview of the state-of-the-art techniques and trends in
different sub-areas of human activity recognition. In the end, we discuss open
research issues and provide future research directions in the field of human
activity recognition.
