  Wearable cameras can gather large a\-mounts of image data that provide rich
visual information about the daily activities of the wearer. Motivated by the
large number of health applications that could be enabled by the automatic
recognition of daily activities, such as lifestyle characterization for habit
improvement, context-aware personal assistance and tele-rehabilitation
services, we propose a system to classify 21 daily activities from
photo-streams acquired by a wearable photo-camera. Our approach combines the
advantages of a Late Fusion Ensemble strategy relying on convolutional neural
networks at image level with the ability of recurrent neural networks to
account for the temporal evolution of high level features in photo-streams
without relying on event boundaries. The proposed batch-based approach achieved
an overall accuracy of 89.85\%, outperforming state of the art end-to-end
methodologies. These results were achieved on a dataset consists of 44,902
egocentric pictures from three persons captured during 26 days in average.
