LEARNING INDIVIDUAL ROLES FROM VIDEO IN A SMART HOME. O. Brdiczka, J. Maisonnasse, P. Reignier, J. L. Crowley

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1 LEARNING INDIVIDUAL ROLES FROM VIDEO IN A SMAR HOME O. Brdczka, J. Masonnasse, P. Regner, J. L. Crowley PRIMA research group, INRIA Rhône-Alpes, France Abstract: hs paper addresses learnng and recognton of ndvdual roles from vdeo data n a smart home envronment. he proposed approach s part of a framework for acqurng a hgh-level contextual model for human behavour n an ntellgent envronment. he proposed methods for role learnng and recognton are based on Bayesan models. he nput s the targets and ther propertes generated and tracked by a robust vdeo trackng system n the envronment. he output s the roles walkng, standng, sttng, nteractng wth table, sleepng for each target. A Bayesan classfer produced good results for a framewse classfcaton of these roles, whle a hdden Markov Model had even better performance takng nto account a pror probabltes of roles and role transtons. A support vector machne produced best classfcaton results. he classfers had, however, problems to dstngush ambguous roles lke walkng and standng n the envronment. he obtaned results permt to pass to the next step n future work: learnng and recognzng relatons and stuaton. Copyrght 2006 INRIA Rhône-Alpes Keywords: context model, role learnng, smart home envronment, Bayesan classfer, hdden Markov model, support vector machnes. INRODUCION Pervasve and ubqutous computng (Weser, 996) ntegrates computaton nto every-day envronments. he technologcal progress of the last decade has enabled computerzed spaces equpped wth multple sensor arrays, lke mcrophones or cameras, and multple human-computer nteracton devces. An early example s the KdsRoom (Bobck et al., 999), a perceptuallybased, nteractve playspace for chldren developed at MI. Smart home envronments (Brumtt et al., 2000) and even complete apartments equpped wth multple sensors (Cook et al., 2003) have been realzed. he major goal of these ntellgent envronments s to enable devces to sense changes n the envronment and to automatcally adapt and act based on these changes. A man focus s lad on sensng and respondng to human actvty. Human actors need to be dentfed and ther current actvty needs to be recognzed. Addressng the rght user at the correct moment, whle percevng hs correct actvty, s essental for correct human-computer nteracton n ntellgent envronments. Intellgent envronments have enabled the computer observaton of human (nter)acton wthn the envronment. he analyss of (nter)actons of two and more ndvduals s here of partcular nterest as t provdes nformaton about socal context and relatons and t further enables computer systems to follow and antcpate human (nter)acton. he latter s a dffcult task gven the fact that human actvty s stuaton dependent (Suchman, 987) and does not necessarly follow plans. Computerzed spaces and ther devces need hence to use ths stuatonal nformaton,.e. context (Dey, 200), to respond correctly to human actvty. In order to become context-aware, computer systems must thus mantan a model descrbng the envronment, ts occupants and ther actvtes. he noton of context s not new and has been explored n dfferent areas lke lngustcs, natural language processng and knowledge representaton. Dey defnes context as any nformaton that can be used to characterze the stuaton of an entty (Dey, 200). An entty can be a person, place or object consdered relevant to user and applcaton. he structure and representaton of ths nformaton must be determned before beng exploted by a specfc applcaton. Context and actvty are separable. he context descrbes features of the envronment wthn whch the actvty takes place (Doursh, 2004). Loke states that stuaton and actvty are, however, not nterchangeable, and actvty can be consdered as a type of contextual nformaton whch can be used to characterze a stuaton (Loke, 2005). Dey defnes stuaton as descrpton of the states of relevant enttes (Dey, 200). Crowley et al. ntroduce then the concepts of role and relaton n order to characterze a stuaton (Crowley et al., 2002). Roles nvolve only one entty, descrbng ts actvty. An entty s observed to play a role. Relatons are defned as predcate functons on several enttes, descrbng the relatonshp or nteracton between enttes playng roles.

2 Acceptance tests determne whether a partcular entty plays a role or whether several enttes are n relaton. hese acceptance tests assocates roles and relatons wth relevant enttes. Context: Lecture Room Empty Audence Lecture Audence (Entty) Lecture (Entty ) Audence ( Entty2 ) Entty NotSameAs Entty2 Fg.. Example of a smple stuaton model for a lecture room. Empty, Audence and Lecture are the avalable stuatons. Lecturer, Audence are the avalable roles and NotSameAs the avalable relaton. Context s fnally represented by a network of stuatons (Crowley et al., 2002). hese stuaton networks have been used to mplement context for dfferent applcatons (Brdczka et al., 2006) (see Fg. for a smple example). hese stuaton models have so far been hand-crafted by experts. However, lttle work has been done on the automatc acquston,.e. learnng, of these hgh-level models from data. Many approaches for learnng basc human actvty models from sensor data have been proposed n recent years. Most of the prevous work s based on vsual nformaton (Zadenberg et al., 2006; Rbero et al., 2005; Olver et al., 2000) or audo nformaton (Brdczka et al., 2005) usng statstcal models for learnng and recognton (n partcular Hdden Markov Models). Some projects focus on supplyng approprate system servces to the users (Cook et al., 2003; Brumtt et al., 2000; Bobck et al., 999), whle others focus on the correct classfcaton of actvtes (Brdczka et al., 2005; ; Rbero et al., 2005; Muehlenbrock et al., 2004). We can dstngush several specfc applcaton domans: survellance (Zadenberg et al., 2006; Rbero et al., 2005; Olver et al., 2000), workplaces (Brdczka et al., 2005; Muehlenbrock et al., 2004), home envronments (Cook et al., 2003;Brumtt et al., 2000) and group entertanment (Bobck et al., 999). However, most work does not attempt to acqure a hgh-level contextual model of human behavour. he man focus s lad on the classfcaton of basc human actvtes or scenaros wthout consderng a rcher contextual descrpton. In ths paper, we want to nvestgate the learnng and recognton of ndvdual roles from vdeo data n a smart home envronment. Roles can be nterpreted here as referrng to basc actvty of ndvduals n the scene. hs problem s part of the attempt to automatcally acqure a rch contextual model for human behavour n a smart home (Fg. 2). Once the roles of ndvduals n the scene and the relatons between them are learned and recognzed, the potental stuatons can be determned. hese stuatons can then be assgned to dfferent scenaros that take place n the envronment. Learnng roles for ndvduals can thus be seen as frst step of the whole acquston process. In ths paper, we propose an approach usng Bayesan classfer, hdden Markov models or support vector machnes for learnng and recognzng these ndvdual roles from vdeo data n smart home envronment. he obtaned results are very good and permt to pass to the next step n future work: learnng and recognzng relatons and stuaton. Indvdual Group roles stuaton scenaro relatons Fg. 2. Relatonshps between role, relaton, stuaton and scenaro. APPROACH In the followng, we present an approach for role recognton from vdeo. Frst, our smart home envronment and the robust vdeo trackng system are brefly depcted. hen, the role labels are shown and data sets are descrbed. Fnally, the Bayesan classfer, the hdden Markov model and the support vector machne method are explaned and the results for the data sets are presented. Smart Home Envronment Smart envronments combne two knds of capactes: percepton and acton. Percepton comprses the nterpretaton of vsual and audo sgnals emtted by the user, whle acton refers to servces provded by wthn the smart envronment. A contextual model s necessary to descrbe possble stuatons n the scene. he model s

3 drven by percepton and controls the servces to provde as well as the nteracton wth the user. targets can be detected by energy measurements based on background subtracton or ntensty normalzed colour hstograms. Fg. 3. Map of our Smart Room (left), map wth wdeangle camera vew (rght). In ths paper, experments take place n a smart home envronment whch s a room equpped lke a lvng room. he set of furnture s composed by small table placed n centre of three armchars and one couch (Fg. 3 left). Mcrophone arrays and vdeo cameras are mounted aganst walls n the envronment. As we focus n ths paper on vdeo, the set of sensors used for our approach s lmted to one wde-angle vdeo camera mounted aganst one corner of the smart room (Fg. 3 rght). Fg. 5. Archtecture of the robust trackng system. he robust trackng module s a form of Kalman flter (Welch and Bshop, 2004) operatng on the lst of current targets. For each target a search regon and a Gaussan mask centred on the most lkely poston s determned usng a frst order Kalman flter. he targets are then updated by collectng data from the detecton module that processes the search regon and computes frst and second moments of the energy mage weghted by the Gaussan mask. he Gaussan mask makes the trackng robust to outlers. Fg. 4. Wde-angle camera mage of the Smart Room. he wde-angle camera records mages of the envronment (Fg. 4) wth a frame rate between 5 and 20 mages per second. A real-tme robust trackng system detects and tracks targets n the vdeo mages. Vdeo rackng System he vdeo trackng system (Capoross et al., 2004) employs a supervsory controller to dynamcally control the selecton of processng modules and the parameters used for processng (Fg. 5). hs system employs multple pxel level detecton operatons to detect and track blobs at vdeo rate. Usually, blobs correspond to movng users n the envronment. A central supervsor s used to adapt processng parameters so as to mantan relable real-tme trackng. Wthn a detecton level Fg. 6. arget propertes returned by the system. he vdeo trackng system returns events n form of vectors for each vdeo frame. Each vector contans the propertes of one target detected and tracked by the system. he returned propertes for each target are top poston (x, y) of the boundng ellpse, the frst and second moment of the ellpse and the angle of between these moments (Fg. 6). Addtonal features lke velocty, speed or energy can also be extracted. As poston and sze of the targets tracked by our robust trackng system always slghtly change, these derved features are very nosy and at present not convenent for learnng. We wll, however, consder these features n future work. Indvdual Roles Before consderng group actvty, each ndvdual user actvty s solated and analyzed. We propose to categorze ths ndvdual actvty nto several basc

4 classes. We refer to these classes by ndvdual roles played by an ndvdual n the envronment. he fve elementary roles that we want to recognze n ths approach are: walkng (Fg. 7), standng (Fg. 8), sttng (Fg. 9), nteracton wth table (Fg. 0) and sleepng (Fg. ). Fg. 0. Elementary role: nteractng wth table. Fg. 7. Elementary role: walkng. Fg.. Elementary role: sleepng. Fg. 8. Elementary role: standng. Recognzng these elementary ndvdual roles s a frst step when recognzng hgh-level behavour. Roles played by enttes and relatons between enttes combne to stuatons. Stuatons are part of a scenaro takng place n the envronment. In the followng, we wll focus on the frst step of ths process: learnng and recognzng ndvdual roles. Data Sets In order to develop and evaluate the recognton process, we recorded 8 short vdeo sequences n the envronment. Durng these sequences, one or several ndvduals played dfferent elementary roles n the smart room. he number of frames and the number of dfferent roles played durng the sequences are ndcated n able. able. Vdeo sequence recordngs Fg. 9. Elementary role: sttng. Vdeo Sequence No. Frames No. Roles

5 he roles played by the ndvduals n the vdeo sequences have been labelled by a supervsor. he supervsor assgned a role label to each target detected by the robust trackng system for each frame. he supervsor could also assgn a no role label f a detected target dd not play one of the fve elementary roles. Each of the 8 data sets contans thus a lst of target propertes (x, y, frst moment, second moment, angle) and the assocated role label. Learnng and Recognzng Roles By usng machne learnng methods, our system s to fnd a connecton between the sensed nformaton (target propertes per frame) and the roles as perceved and labelled by the supervsor. We are focusng partcularly on Bayesan methods, because they are well adapted to deal wth erroneous sensor data and they have proven to be useful n many applcaton domans, n partcular computer vson (Zadenberg et al., 2006; Rbero et al., 2005; Olver et al., 2000). In the followng, we wll present three methods based on Bayesan classfer, hdden Markov models and support vector machnes. Bayesan Classfer. On the bass of the sensor data and the assocated role labels, we seek to learn a probablstc classfer for relevant roles. he proposed Bayesan classfer s smlar to classfers proposed n (Rbero et al., 2005; Muehlenbrock et al., 2004). he classfcaton s done framewse,.e. the classfer takes the target propertes of one frame as nput and generates the role predcton for the frame as output. We seek to determne the role r MAP wth the maxmum a posteror (MAP) probablty, gven the target property set (equaton ()). rmap = argmax r ) () r r) r) P ( r ) = (2) ) We apply Bayesan theorem (2) and we further assume that the pror probabltes r) for the roles are equal for each frame. As the constant denomnator can be elmnated because of the argmax, we get (3). rmap = argmax r) (3) r We model r) for each role as multdmensonal Gaussan mxture dstrbuton estmated by runnng EM algorthm (Blmes, 998) on the learnng data. he ntal number nb_g of Gaussans n the mxture s assessed usng formula (4), where 8 s an expermental value. Gaussans wth a too weak contrbuton to the mxture are elmnated. dm nb _ g = round( 8 ) (4) We evaluated the classfer on the vdeo sequence recordngs (able ) usng 8-fold cross-valdaton. Each sequence has been used for testng once, whle learnng the model wth the 7 remanng sequences. he average classfcaton results can be seen n form of confuson matrces n able 2, able 3 and able 4. able 2. Confuson matrx for Bayesan classfer wth = (X, Y) Walkng Standng Sttng Inter. table Sleepng Walkng Standng Sttng Inter. table Sleepng We evaluated three dfferent target property sets. he frst set was the poston X, Y n the mage. he results are good (able 2) showng that the poston n the envronment s dscrmnatng for ndvdual roles. Poston s, however, very dependent on envronment confguraton, e.g. couch and char localsaton. herefore, the second target set was (st, 2nd, angle), whch only contans nformaton on the form of the ellpse and not ts poston. he results (able 3) are qute smlar to those obtaned for the poston. able 3. Confuson matrx for Bayesan classfer wth = (st, 2nd, angle) Walkng Standng Sttng Inter. table Sleepng Walkng Standng Sttng Inter. table Sleepng he combnaton of the frst and second target property sets (X, Y, angle, st, 2nd) gves the best results (able 4). In general, ambguous roles lke sttng and nteractng wth table or n partcular walkng and standng are dffcult to dstngush for each frame (even for a human supervsor!), whch leads to numerous wrong classfcatons. able 4. Confuson matrx for Bayesan classfer wth = (X, Y, angle, st, 2nd) Walkng Standng Sttng Inter. table Sleepng Walkng Standng Sttng Inter. table Sleepng

6 he overall results of the Bayesan classfer can be seen n the left column of able 9. Hdden Markov Model. In order to mprove recognton results, we want to take nto account role frequency and the relatonshp between roles. herefore, we use a hdden Markov model (Rabner, 987) as classfer for the roles. he classfcaton s done on a sequence,.e. the classfer takes a sequence of target propertes as nput and generates a sequence of role predctons as output. A hdden Markov model (HMM) s a stochastc process where the evoluton s managed by states. he seres of states consttute a Markov chan whch s not drectly observable. hs chan s hdden. Each state of the model generates an observaton. Only the observatons are vsble. he objectve s to derve the state sequence and ts probablty, gven a partcular sequence of observatons. In our case, the observatons are the target property set values, whle the state values are the ndvdual roles. Equaton (5) calculates the probablty of target property set sequence n and role sequence r r n. r ) r... r 2 n ; r... r r ) ) 2 n r ) = 2 )... r n r n ) As for the Bayesan classfer, we model r) as multdmensonal Gaussan mxture dstrbuton estmated by EM. he a pror probabltes r) of the roles are estmated from the role frequences n the learnng sets. he transton probabltes r r j ) between the roles are estmated from the transton frequences (smoothed by smple LaPlace). n r n ) (5) able 5. Confuson matrx for HMM wth = (X, Y) Walkng Standng Sttng Inter. table Sleepng Walkng Standng Sttng Inter. table Sleepng In order to derve the state (role) sequence and ts probablty from a gven observaton (target property set) sequence and the HMM, we used the Vterb algorthm (Rabner, 987). As for the Bayesan classfer, we conducted a 8-fold cross-valdaton on the vdeo sequence recordngs. he average classfcaton results of the HMM can be seen n form of confuson matrces n able 5, able 6, and able 7. able 6. Confuson matrx for HMM wth = (st, 2nd, angle) Walkng Standng Sttng Inter. table Sleepng Walkng Standng Sttng Inter. table Sleepng Agan we evaluated three dfferent target property sets : (X, Y), (st, 2nd, angle) and (X, Y, angle, st, 2nd). As for the Bayesan classfer, the combnaton of target property sets (X, Y) and (st, 2nd, angle) produced best results (able 7). able 7. Confuson matrx for HMM wth = (X, Y, angle, st, 2nd) Walkng Standng Sttng Inter. table Sleepng Walkng Standng Sttng Inter. table Sleepng he overall results of the HMM are depcted n the mddle column of able 9. As expected, the HMM shows better performance as the Bayesan Classfer due to addtonal a pror probabltes and temporal smoothng. However, the ambguty of roles, n partcular between walkng and standng, perssts, resultng n a poor precson for walkng and a poor recall for standng. Support Vector Machnes. In order to further mprove recognton results, we use support vector machnes (SVMs) as classfer. As for the Bayesan classfer, the classfcaton s done framewse,.e. the SVMs take the target propertes of one frame as nput and generate the role predcton for the frame as output. SVMs classfy data through determnaton of a set of support vectors, through mnmzaton of the average error. he support vectors are members of the set of tranng nputs that outlne a hyperplane n feature space. hs l-dmensonal hyperplane, where l s the number of features of the nput vectors, defnes the boundary between the dfferent classes. he classfcaton task s smply to determne on whch sde of the hyperplane the testng vectors resde.

7 Agan we evaluated the classfer on the vdeo sequence recordngs (able ) usng 8-fold cross-valdaton. A radal bass functon kernel wth C=.0 and γ =.0 showed good results for our tranng data. As SVM algorthms try to fnd the optmal hyperplane n a multdmensonal space, no addtonal feature selecton s necessary. We used the LIBSVM lbrary (Chang and Ln, 200); the average classfcaton results of the 8-fold cross-valdaton are depcted n able 8. able 8. Confuson matrx for SVMs wth = (X, Y, angle, st, 2nd) Fg. 2. SVM classfer hyperplane and margns for a tranng set of two classes ( and ). Gven a tranng set of nstance-label pars (x, y ), = l where x Є R n and y Є {, } (two class problem), the support vector machnes (Boser et al., 992; Cortes and Vapnk, 995) requre the soluton of the followng optmzaton problem: l mn ω ω + C ξ ω, b, ξ 2 (6) subject to = y ( ω φ( x ) + b) ξ, ξ 0 Here tranng vectors x are mapped nto a hgher (maybe nfnte) dmensonal space by the functon φ. hen the SVM fnds a lnear separatng hyperplane wth the maxmal margn n ths hgher dmensonal space (Fg. 2). C > 0 s the penalty parameter of the error term. K(x, x j ) = φ x ) φ( x ) s called the kernel functon. ( j hough new kernels are beng proposed by researchers, there are four basc kernels: Lnear: K(x, x j ) = x x j d Polynomal: K(x, x j ) = ( γ x x + r), γ > 0 Radal bass functon (RBF): K(x, x j ) = exp( γ x x ), γ > 0 Sgmod: K(x, x j ) = tanh( γ x x r) γ, r and d are kernel parameters. j 2 j j + For mult-class classfcaton, a one-aganst-one classfcaton for each of the k classes can be done. K*(K-)/2 classfers are then generated to tran the data, where each tranng vector s compared aganst two dfferent classes and the error (between the separatng hyperplane margn) s mnmzed. he classfcaton of the testng data s accomplshed by a votng strategy, where the wnner of each bnary comparson ncrements a counter. he class wth the hghest counter value after all classes have been compared s selected. Walkng Standng Sttng Inter. table Sleepng Walkng Standng Sttng Inter. table Sleepng he overall results of SVM are shown n the rght column of able 9. As SVM s a dscrmnatve method, t optmzes classfcaton between the gven/traned classes, outperformng Bayesan Classfer and HMM. However, SVM does not learn the structure of the gven data (but only borders and margns between classes), whch makes t dffcult/mpossble to reject unseen test data ( garbage ) or to dscover new classes of roles. As for Bayesan classfer and HMM, the ambguty of roles, n partcular between walkng and standng, perssts. able 9. Overall recognton rates for Bayesan classfer, HMM and SVM Bayesan Cl. HMM SVM X,Y st, 2nd, angle X,Y, angle, st, 2nd CONCLUSION AND FUURE WORK We presented an approach for learnng and recognzng ndvdual roles from vdeo n a smart home envronment. he approach s part of a framework for acqurng a hgh-level contextual model for human behavour n an ntellgent envronment. Role recognton s the backbone of ths framework as roles are necessary for determnng relatons between enttes, current stuaton and scenaro. he proposed methods for role learnng and recognton are based on Bayesan models and support vector machnes. he nput for learnng and classfcaton are the targets and ther propertes tracked by a robust vdeo trackng system n the envronment. A Bayesan classfer produced good results for a framewse classfcaton of roles. A hdden Markov Models had even better performance on a sequence classfcaton of roles, takng nto account a pror probabltes of roles and role transtons. Support vector machnes produced best results, outperformng Bayesan classfer and HMM. he classfers had, however,

8 problems to dstngush ambguous roles lke walkng and standng. he obtaned results permt to pass to the next step n future work: learnng and recognzng relatons and stuaton. Future work wll also concern an mprovement of the recognton rate for roles. A frst step s to ntegrate addtonal features nto the target property sets. Poston of face and hands, derved by skn colour detector, velocty, speed and background subtracton energy are prospectve canddates. Velocty, speed and energy estmaton need to be stablzed n order to be robust to nose. An ntegraton of sound and voce recognton mght further be useful for role recognton and n partcular for later relaton and stuaton detecton. In addton, labellng errors due to ambguous roles need to be dentfed and elmnated from learnng sets. hs can be done by usng a Kappa test on the labels of several dfferent supervsors. Furthermore, a garbage role needs to be created permttng to dentfy unseen roles and erroneous sensor values. One possblty s to set a threshold for the probablty calculated for roles and role sequences. Roles recognzed wth probablty below the threshold are then treated as garbage, entalng the learnng of new unseen roles. ACKNOWLEDGEMENS hs work has been funded by the France élécom R&D project HARP. We would lke to thank Glles Prvat and Olver Berner from France élécom for ther remarks and support durng ths project. REFERENCES Blmes, J. A. (998). A Gentle utoral of the EM Algorthm and ts Applcaton to Parameter Estmaton for Gaussan Mxture and Hdden Markov Models. echncal Report ICSI-R-97-02, Unversty of Berkeley. Bobck, A.F., Intlle, S.S., Davs, J.W., Bard, F., Pnhanez, C.S., Campell, L.W., Ivanov, Y.A., Schutte, and Wlson, A. (999). he KdsRoom: A Perceptually-Based Interactve and Immersve Story Envronment. In Presence (USA) 8(4): Boser, B., Guyon, I. and Vapnk, V. (992). A tranng algorthm for optmal margn classfers. In Proceedngs of the Ffth Annual Workshop on Computatonal Learnng heory. Brdczka, O., Regner, P., Crowley, J.L., Vaufreydaz, D., and Masonnasse, J. (2006). Determnstc and probablstc mplementaton of context. In Proceedngs of Fourth IEEE Internatonal Conference on Pervasve Computng and Communcatons Workshops. Brdczka, O., Masonnasse, J., and Regner, P. (2005). Automatc Detecton of Interacton Groups, In Proceedngs of Internatonal Conference of Multmodal Interfaces, pages Brumtt, B., Meyers, B., Krumm, J., Kern, A., and Shafer, S. (2000). Easylvng: echnologes for Intellgent Envronments. In Proceedngs of Second Internatonal Symposum of Handheld and Ubqutous Computng. Lecture Notes n Computer Scence, volume 927, pages Capoross, A., Hall, D., Regner, P., and Crowley, J.L. (2004). Robust vsual trackng from dynamc control of processng. In Proceedngs of Internatonal Workshop on Performance Evaluaton for rackng and Survellance, pages Chang, C.-C. and Ln, C.-J. (200). LIBSVM: a lbrary for support vector machnes. Software avalable at Cook, D. J., Youngblood, M., Heerman, E. O., Gopalratnam, K., Rao, S., Ltvn, A., and Khawaja, F. (2003). MavHome: An Agent-Based Smart Home. In Proceedngs of Frst IEEE Internatonal Conference on Pervasve Computng and Communcatons. Cortes, C., and Vapnk, V. (995). Support-vector network. In Machne Learnng 20: Crowley, J.L., Coutaz, J., Rey, G. and Regner, P. (2002). Perceptual components for context aware computng. In Proceedngs of Fourth Internatonal Conference on Ubqutous Computng. Dey, A.K. (200). Understandng and usng context. In Personal and Ubqutous Computng 5:4-7. Doursh, P. (2004). What we talk about when we talk about context. In Personal and Ubqutous Computng 8:9-30. Loke, S.W. (2005). Representng and reasonng wth stuatons for context-aware pervasve computng: a logc programmng perspectve. In he Knowledge Engneerng Revew 9(3): Muehlenbrock, M., Brdczka, O., Snowdon, D., and Meuner, J.-L. (2004). Learnng to Detect User Actvty and Avalablty from a Varety of Sensor Data. In Proceedngs of Second IEEE Internatonal Conference on Pervasve Computng and Communcatons. Olver, N., Rosaro, B., and Pentland, A. (2000). A Bayesan Computer Vson System for Modelng Human Interactons. In IEEE rans. Pattern Analyss and Machne Intellgence 22(8): Rabner, L. A. (987). A tutoral on Hdden Markov Models and selected applcatons n speech recognton. Proceedngs of IEEE 77(2): Rbero, P., and Santos-Vctor, J. (2005). Human actvty recognton from Vdeo: modelng, feature selecton and classfcaton archtecture. In Proceedngs of Internatonal Workshop on Human Actvty Recognton and Modellng. Suchman, L. (987), Plans and Stuated Actons: he Problem of Human-Machne Communcaton, Cambrdge Unversty Press. Weser, M. (996), Ubqutous Computng: Defnton, l.

9 Welch, G., and Bshop, G. (2004). An ntroducton to the kalman flter. echncal Report R 95-04, Unversty of North Carolna at Chapel Hll. Zadenberg, S., Brdczka, O., Regner, P., and Crowley, J.L. (2006). Learnng context models for the recognton of scenaros. In Proceedngs of hrd IFIP Conference on Artfcal Intellgence Applcatons and Innovatons.

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