<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zaraki, Abolfazl</style></author><author><style face="normal" font="default" size="100%">Giuliani, M</style></author><author><style face="normal" font="default" size="100%">Dehkordi, MaryamBanitalebi</style></author><author><style face="normal" font="default" size="100%">D'ursi, A.</style></author><author><style face="normal" font="default" size="100%">De Rossi, Danilo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An RGB-D based social behavior interpretation system for a humanoid social robot</style></title><secondary-title><style face="normal" font="default" size="100%">Robotics and Mechatronics (ICRoM), 2014 Second RSI/ISM International Conference on </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=6990898&amp;isnumber=6990766</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">185-190</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div id=&quot;article-actions&quot;&gt;
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			&lt;span style=&quot;font-size: 12px;&quot;&gt;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.&lt;/span&gt;&lt;/div&gt;
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