@inproceedings{741c8a10aaae4b6ab9991d5ebcc57177,
title = "An RGB-D based social behavior interpretation system for a humanoid social robot",
abstract = "Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81\% for all states.",
keywords = "hidden Markov model, Human-robot interaction, humanlike robot, social behavior recognition",
author = "Aolfazl Zaraki and Manuel Giuliani and Dehkordi, \{Maryam Banitalebi\} and Daniele Mazzei and Annamaria D'Ursi and \{De Rossi\}, Danilo",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 2nd RSI/ISM International Conference on Robotics and Mechatronics, ICRoM 2014 ; Conference date: 15-10-2014 Through 17-10-2014",
year = "2014",
month = dec,
day = "17",
doi = "10.1109/ICRoM.2014.6990898",
language = "English",
series = "2014 2nd RSI/ISM International Conference on Robotics and Mechatronics, ICRoM 2014",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "185--190",
booktitle = "2014 2nd RSI/ISM International Conference on Robotics and Mechatronics, ICRoM 2014",
address = "United States",
}