DL-DARE: Deep learning-based different activity recognition for the human–robot interaction environment.

HUMAN-robot interaction; DEEP learning; IMAGE sensors; IMAGE processing; FEATURE extraction; CLASSROOM activities

This paper proposes a deep learning-based activity recognition for the Human–Robot Interaction environment. The observations of the object state are acquired from the vision sensor in the real-time scenario. The activity recognition system examined in this paper comprises activities labeled as classes (pour, rotate, drop objects, and open bottles). The image processing unit processes the images and predicts the activity performed by the robot using deep learning methods so that the robot will do the actions (sub-actions) according to the predicted activity. [ABSTRACT FROM AUTHOR]

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