  Human action recognition is used in many applications such as video
surveillance, human computer interaction, assistive living, and gaming. Many
papers have appeared in the literature showing that the fusion of vision and
inertial sensing improves recognition accuracies compared to the situations
when each sensing modality is used individually. This paper provides a survey
of the papers in which vision and inertial sensing are used simultaneously
within a fusion framework in order to perform human action recognition. The
surveyed papers are categorized in terms of fusion approaches, features,
classifiers, as well as multimodality datasets considered. Challenges as well
as possible future directions are also stated for deploying the fusion of these
two sensing modalities under realistic conditions.
