000 06471nas|a2200529 a 4500
005 20260817171147.0
008 161122s2012 th uu|m rtt 0| a1eng d
035 _a.b12175110
099 9 _aAIT Diss. no.CS-12-01
100 1 _aHussain, Akhtar
245 1 0 _aBayesian network based student affect modeling framework for an intelligent tutoring system
260 _aPathum Thani, Thailand :
_bAsian Institute of Technology,
_c2012
300 _a1 online resource (86 p.) :
_bill.
490 1 _aDissertation ;
_vno. CS-12-01
500 _aA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science, School of Engineering and Technology
502 _aThesis (Ph.D.) - Asian Institute of Technology, 2012
520 _a Human beings have the ability to detect and understand affective/mental states, and other social signals when interacting with each other. This ability or intelligence is an important aspect of their social life in social relationships. Researchers from multi-disciplinary areas have been trying to incorporate this ability or intelligence in computers through body lan- guage to make them affective companions of the users for variety of applications. Gestures either intentional or unintentional are very useful to {uFB01}nd the underlying mental or affective state of a person in any social interaction. Researchers developing interactive and intelligent computer interfaces are very much interested in extracting meaningful information from variety of gestures, e.g., self-manipulators that include unintended hand-touch-head (face) movements. Predicting human behavior from physical activity is an active area of research for many Affective Computing applications. However, correctly detecting and classifying human body movements specially unintentional body gestures are a research problem. The main problem is occlusion in unintended hand-touch-head (face) gesture{u2019}s classi{uFB01}cation due to similar skin color and texture because when the hand enters in the face region. it merged with the face so dif{uFB01}cult to separate the hand from the face region. However, we propose a solution for separating hand(s) from face in varying lighting conditions by generating lo- cal binary patterns using force {uFB01}eld features in conjunction with Sobcl edge operator called (Sobel-LBP). In this dissertation we performed two experiments one with single context and second with multi-context scenarios using real and synthetic data. In our {uFB01}rst experiment we used vision based techniques and Bayesian network model for student mental state prediction from un- intentional hand-touch-head (face) movements in classroom context. After successful clas- si{uFB01}cation of the gestures in the form ofbinary codes using vision based techniques, we code these different gestures of more than 100 human subjects. and feed these codes manually in three-layered Bayesian network (BN) to infer the probable mental state with particular gesture. The {uFB01}rst layer shows the mental states to gestures relationships and the second layer combine the gestures, and SLBP generated binary codes. The proposed scheme when eval- uated on a our data set in single context scenario collected in real classroom situation and found promising results with an accuracy of about 85%. The framework will be utilized for developing intelligent tutoring system. In our second experiment we used same techniques for predicting the student mental state in classroom context with multi-context scenarios using real and synthetic data and obtained 75% average accuracy of the predicted result. This result can be improved by increasing the collection of data sets in different contexts in real time situations for training and testing. The results show that our proposed system can also be used for various other applications of Affective Computing. Our proposed system exhibits that in single and as well as in multi-contexts situations. un- intentional body gestures can carry useful information about the mental or affective state of the students that can be used to increase the ef{uFB01}ciency of various applications such as an in- telligent tutoring system by applying an integrated framework of computer vision techniques and Bayesian network model.
650 0 _aIntelligent tutoring systems
650 0 _aBayesian statistical decision theory
700 1 _aAfzulpurkar, Nitin V.,
_eChairperson
700 1 _aGuha, Sumanta,
_eExamination Committee
700 0 _aManukid Parnichkun,
_eExamination Committee
700 1 _aAbbasi, Abdul Rehman,
_eExamination Committee
710 2 _aHigher Education Commission (HEC), Pakiatan,
_eScholarship Donor
710 2 _aAsian Institute of Technology Fellowship,
_eScholarship Donor
810 2 _aAsian Institute of Technology.
_tDissertation ;
_vno. CS-12-01
856 _3Full-Text
_uhttp://203.159.5.9/ait-thesis/detail.php?q=B00288
907 _a.b12175110
_bmnait
_cm
902 _a240409
998 _b0
_c161122
_dm
_ea
_fm
_g0
962 _a000:001:PDF:b1217511:002821:0:0:0:0:0:0
_tAbstract-AIT Diss. no.CS-12-01
_vn
945 _lmnait
945 _lmnait
945 _lmnarc
945 _lmnarc
945 _lmnarc
945 _lmnarc
942 _c20
942 _c40
909 _aBarcode : 30050120388813
_bCREATED : 2016-11-22
_cRECORD # : i13061884
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
909 _aBarcode : 30050120388805
_bCREATED : 2016-11-22
_cRECORD # : i13061896
_dLPATRON : 1027170
_eLCHKIN : 2019-11-07
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 3
_jTOT RENEW : 4
909 _aBarcode : 30050120405963
_bCREATED : 2017-05-04
_cRECORD # : i13073576
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
909 _aBarcode : 30050120978258
_bCREATED : 2020-09-17
_cRECORD # : i13296991
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
909 _aBarcode : 30050120978241
_bCREATED : 2020-09-17
_cRECORD # : i13297004
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
909 _aBarcode : 30050120978233
_bCREATED : 2020-09-17
_cRECORD # : i13297016
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
999 _c11162
_d11162