Primitive activity recognition from short sequences of sensory data

Activity recognition from mobile device sensors and wearables is attracting more attention from the research community due to the widespread adoption of these devices and the unique opportunity they provide for understanding user’s behavior leading to novel services and improvements in the delivery of existing ones. Approaches to tackle this problem either rely on predefined statistical features of sensor data streams or feature learning with the latter providing higher accuracies in most cases. Deep learning methods proved more effective than traditional approaches to feature learning in multiple studies. This paper presents a novel end-to-end trainable deep architecture that utilizes multiple convolutional neural networks (CNN), late fusion and extensive layer bypassing. The proposed method can easily accommodate multiple sensors and signal representations. The proposed approach is validated on eight publicly available datasets using a variety of evaluation conditions showing that it outperforms state-of-the-art methods in six of them.