  Human activity recognition based on mobile device sensor data has been an
active research area in mobile and pervasive computing for several years. While
the majority of the proposed techniques are based on supervised learning,
semi-supervised approaches are being considered to significantly reduce the
size of the training set required to initialize the recognition model. These
approaches usually apply self-training or active learning to incrementally
refine the model, but their effectiveness seems to be limited to a restricted
set of physical activities. We claim that the context which surrounds the user
(e.g., semantic location, proximity to transportation routes, time of the day)
combined with common knowledge about the relationship between this context and
human activities could be effective in significantly increasing the set of
recognized activities including those that are difficult to discriminate only
considering inertial sensors, and the ones that are highly context-dependent.
In this paper, we propose CAVIAR, a novel hybrid semi-supervised and
knowledge-based system for real-time activity recognition. Our method applies
semantic reasoning to context data to refine the prediction of a
semi-supervised classifier. The context-refined predictions are used as new
labeled samples to update the classifier combining self-training and active
learning techniques. Results on a real dataset obtained from 26 subjects show
the effectiveness of the context-aware approach both on the recognition rates
and on the number of queries to the subjects generated by the active learning
module. In order to evaluate the impact of context reasoning, we also compare
CAVIAR with a purely statistical version, considering features computed on
context data as part of the machine learning process.
