  Daily life activities, such as eating and sleeping, are deeply influenced by
a person's culture, hence generating differences in the way a same activity is
performed by individuals belonging to different cultures. We argue that taking
cultural information into account can improve the performance of systems for
the automated recognition of human activities. We propose four different
solutions to the problem and present a system which uses a Naive Bayes model to
associate cultural information with semantic information extracted from still
images. Preliminary experiments with a dataset of images of individuals lying
on the floor, sleeping on a futon and sleeping on a bed suggest that: i)
solutions explicitly taking cultural information into account are more accurate
than culture-unaware solutions; and ii) the proposed system is a promising
starting point for the development of culture-aware Human Activity Recognition
methods.
