Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition
This addresses the challenge of cultural variability in automated activity recognition for applications like smart homes, but it is incremental as it builds on existing methods.
The paper tackled the problem of improving vision-based human home activity recognition by incorporating cultural information, finding that culture-aware solutions were more accurate than culture-unaware ones in preliminary experiments.
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.