  Collaborative robots are increasingly present in industry to support human
activities. However, to make the human-robot collaborative process more
effective, there are several challenges to be addressed. Collaborative robotic
systems need to be aware of the human activities to (1) anticipate
collaborative/assistive actions, (2) learn by demonstration, and (3) activate
safety procedures in shared workspace. This study proposes an action
classification system to recognize primitive assembly tasks from human motion
events data captured by a Dynamic and Active-pixel Vision Sensor (DAVIS).
Several filters are compared and combined to remove event data noise. Task
patterns are classified from a continuous stream of event data using advanced
deep learning and recurrent networks to classify spatial and temporal features.
Experiments were conducted on a novel dataset, the dataset of manufacturing
tasks (DMT22), featuring 5 classes of representative manufacturing primitives
(PickUp, Place, Screw, Hold, Idle) from 5 participants. Results show that the
proposed filters remove about 65\% of all events (noise) per recording,
conducting to a classification accuracy up to 99,37\% for subjects that trained
the system and 97.08\% for new subjects. Data from a left-handed subject were
successfully classified using only right-handed training data. These results
are object independent.
