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<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b>Master Thesis Defense
<o:p></o:p></b></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b><span style="color:black">Computer Science Program<o:p></o:p></span></b></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b><span style="color:black">Department of Computing Sciences<o:p></o:p></span></b></p>
<p class="MsoNormal" style="line-height:150%"><b><span style="color:black"><o:p> </o:p></span></b></p>
<p class="MsoNormal" align="center" style="margin-left:.5in;text-align:center;text-indent:.5in;line-height:150%">
<b><span style="color:black">TITLE</span></b><span style="color:black">: </span>REAL-TIME HUMAN MOVEMENT PREDICTION BASED ON HUMAN HABITS AND EMOTION<o:p></o:p></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b><span style="color:black">SPEAKER</span></b><span style="color:black">: Roberto Rodriguez</span><o:p></o:p></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b><span style="color:black">THESIS</span></b><span style="color:black">
<b>CHAIR</b>: Dr. Longzhuang Li</span><o:p></o:p></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b><span style="color:black">THESIS COMMITTEE</span></b><span style="color:black">: Dr. Dulal Kar and Dr. Chen Pan</span><o:p></o:p></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b><span style="color:black">DATE</span></b><span style="color:black">: Friday, July 9, 2021</span><o:p></o:p></p>
<p class="MsoNormal" align="center" style="text-align:center;line-height:150%"><b><span style="color:black">TIME</span></b><span style="color:black">: 10:30 am</span><o:p></o:p></p>
<p class="MsoNormal" align="center" style="text-align:center"><b><span style="color:black">PLACE</span></b><span style="color:black">: WebEx</span><o:p></o:p></p>
<p class="MsoNormal" align="center" style="margin-left:1.0in;text-align:center;text-indent:.5in;line-height:18.0pt">
<span style="color:#333333"><a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftamucc.webex.com%2Ftamucc%2Fj.php%3FMTID%3Dm6e121b9df418dd622e464e6faae9e15e&data=04%7C01%7Ccosc-grad-students-list%40listserv.tamucc.edu%7Cc4c92d784fca419df4df08d942290b65%7C34cbfaf167a64781a9ca514eb2550b66%7C0%7C0%7C637613566325480401%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=VWFonaL6eqH5L%2BCVF1C6GKhsf5m2%2FsFyLSxHPuEuo5M%3D&reserved=0" originalsrc="https://tamucc.webex.com/tamucc/j.php?MTID=m6e121b9df418dd622e464e6faae9e15e" shash="jsYvQlLf33za5FEBv/f6mRCcSYU/7tQukY4ZkIgYmKIotVCu055rqSKzi3g8aYnHWwdqXhpvtxh9FsZvWH1j6hXo5Gw1CPq7fJICEhmwvCgcuNAyieEsRSZ8z8ZZ1SWEGXiK46LZWGLoKY550HLX8KeTCex4gIBCsGoqLwoVv+4="><span style="color:blue">https://tamucc.webex.com/tamucc/j.php?MTID=m6e121b9df418dd622e464e6faae9e15e</span></a></span><span style="font-size:12.0pt;font-family:"Times New Roman",serif;color:#333333"><o:p></o:p></span></p>
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Meeting number (access code): 177 885 2704<o:p></o:p></p>
<p class="MsoNormal" align="center" style="margin-left:1.0in;text-align:center;text-indent:.5in">
Meeting password: pdVvpGGW457 (73887449 from phones)<o:p></o:p></p>
<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:12.0pt;color:black"><o:p> </o:p></span></b></p>
<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:12.0pt;color:black">ABSTRACT</span></b><o:p></o:p></p>
<p class="MsoNormal"> This research project develops a new deep neural network model for real-time human movement prediction based on both human habits and emotion by layering multiple neural networks. By combining multiple residual
neural networks (ResNets) using different algorithms on multiple datasets, the new model can increase prediction accuracies and reduce errors because each neural network adjusts to fill in the gaps left behind by the previous neural networks and averages out
a proper evaluation of human motion. To be more specific, the new model contains the following components, ResNet34 on Pascal3d+ for motion, FURIA algorithm for emotion, and nearest neighbor neural network for prediction. The result was that the prediction
would take 0.05 milliseconds after emotion is selected to start its prediction paths. It does follow the motion at first but with lower accuracy until emotion is given. As its database grew, the accuracy grew as well, leading the prediction to near match real-time
movement.<o:p></o:p></p>
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