User Behavior Recognition based on Clustering for the Smart Home

Transcription

1 3rd WSEAS Internatonal Conference on REMOTE SENSING, Vence, Italy, Noveber 2-23, User Behavor Recognton based on Clusterng for the Sart Hoe WOOYONG CHUNG, JAEHUN LEE, SUKHYUN YUN, SOOHAN KIM* AND EUNTAI KIM Sasung Electroncs, Network Sys. Dv. Internet Infra Tea* School of Electrcal and Electroncs Engneerng Yonse Unversty 34 Shnchon-dong, Seodaeun-gu, Seoul KOREA Abstract: - In the vson of ubqutous coputng envronent, sart obects would councate each other and provde any knds of nforaton about user and ther surroundngs n the hoe. Ths nforaton enables sart obects to recognze user behavors and to provde actve and convenent servces to the custoers. However n ost cases, contet-aware servces are avalable only wth epert systes. The proposed ethod presents general contet-aware syste n the sart hoe based on a Bayesan network (BN), whch enables us handlng user contet probablstcally. We used fuzzy c-eans algorth for clusterng user postons and actve te to splfy BN s structure and to reduce BN's condtonal probablty table sze. We used a vrtual sart hoe wth web based sulator to collect saples of user actvtes and ther envronents. Ths suggested algorth was sulated wth 0 fold cross-valdaton and evaluated to verfy the perforance of contet-awareness. Key-Words: - Behavor recognton, Contet-aware, Sart hoe, Bayesan network, Fuzzy c-ean clusterng Introducton Coputaton and access to the nternet are now avalable n everywhere wth the wreless technology, whch fully nterconnects all the fed or portable coputng devces. The purpose of ths work s to provde ore natural nteracton between huan and sart hoe to recognze user behavor, whch eans contet-awareness. For contet-aware applcatons, Want[] proposed Actve Badge locaton syste, whch enables call forwardng accordng to user locaton. Tapa[2] used sple and ubqutous sensors for actvty recognton. There are also any achne learnng approaches n the sensor-based contet-aware syste. For eaple Ranganathan[3] used probablstc logc, fuzzy logc and Bayesan networks to reason about uncertantes n pervasve coputng envronents. Laerhoeven and Cakakc report on usng Kohonen aps, K Nearest Neghbor classfcaton (KNN) and Markov chans to detect user actvty. We developed a user behavor recognton odel based on a Bayesan Network (BN). However these knds of ethods requre soe odfcaton by user or eperts. So we propose ore natural nterface between user and hoe based on splfed BN and fuzzy clusterng algorth. The BNs are one of the best known ethods to reason under uncertanty. In addton, the graphcal nature of BNs gves us a uch better ntutve grasp of the relatonshps aong the features [4]. The BN denotes ont probablstc dstrbuton aong varables of nterest based on ther probablstc relatonshps. The structure of BN s a drected acyclc graph (DAG). Each node represents a rando varable whch has a fnte dscrete data set of doan and s connected wth ts parent s nodes. Each arc represents the condtonal dependency between the parent and the descent. The fuzzy c-eans(fcm) clusterng algorth s an clusterng technque whch s separated fro hard c-eans that eploys hard parttonng. The FCM eploys fuzzy parttonng such that a data pont can belong to all groups wth dfferent ebershp grades between 0 and [5]. The rest of the paper s organzed as follows: We brefly ntroduce the Bayesan network and fuzzy c-eans algorth n Secton 2. In Secton 3, we descrbe the odelng of user behavor recognton we address. In Secton 4, we descrbe the sulaton we have perfored to eplore the perforance of the ethod and eane the results. We conclude and dscuss our future work n Secton 5.

2 3rd WSEAS Internatonal Conference on REMOTE SENSING, Vence, Italy, Noveber 2-23, Prelnares 2. Bayesan Networks A Bayesan network s a graphcal odel that cobnes graph theory, probablty theory and statstcs[4]. In the graph theory a drected graph coposed of (V,E), where the eleents of V are called nodes and the set of edges, E s ordered pars, whch represent condtonal dependences between nodes. There would be a cycle n the drected graph. A cycle s a set of edges, where the start and end nodes are the sae. On the other sde drected acyclc graph (DAG) s a drected graph wth no cycles and the BNs are also DAG. Suppose that G=(V,E) s a DAG. If all the nodes are condtonally ndependent of ther non-descendant nodes gven ther parent nodes, then the DAG satsfes the Markov condton. Ths Markov condton allows us to represent the probablty of the whole network wth the ont dstrbuton of the nodes. For eaple the probablty of the whole network shown n Fgure s gven by P( A, B, C, D, E, F, G ) = P( A) P( B) P( C ) P( D A, B) P( E B, C ) () P( F D) P( G E ) The learnng BN conssts of structure learnng and paraeter learnng. In the structure learnng, we fnd optal connectons of between the nodes. The ost wdely used algorth for structure learnng ethod s K2 learnng, whch was developed Coopeer and Herskovts [6]. In the paraeter learnng, we calculate the condtonal probablstc dstrbuton for the gven BN structure and saples of data. The ost popular paraeter learnng ethod s EM algorth [7]. 2.2 Fuzzy c-eans algorth Fuzzy c-eans (FCM) s a ethod of fuzzy clusterng algorth whch allows one pece of data to belong to ore than one cluster. Ths ethod was developed by Dunn n 973 and proved by Bezdek[5] n 98. Fgure 2 shows the dfference of hard and fuzzy c-eans algorth wth splfed -dentonal eaples. Snce t can handle abguty of clusters, ths algorth s frequently used n pattern recognton. The fundaental prncples s nzaton of the followng obectve functon J N C = = = u c 2, (3) where s fuzzy eponent, whch s any real nuber greater than, u s the degree of ebershp of n the cluster, s the th of d-densonal easured data, c s the d-denson center of the cluster, and * s any nor epressng the slarty between any easured data and the center. Fg.. Eaple of a Bayesan network e bershp functon Ths DAG enables us to present Bayesan rules as splfed graph odel, Bayesan network. In the BN each node represents a rando varable, and the DAG represents the ont probablty dstrbuton of a set of rando varables, X={X, X 2,, X n }. We assue that a varable X s ndependent of the other varables n the network, gven ts parents π, and the attrbute of each node s fnte and dscrete value. Usng ths assupton, the ont probablty dstrbuton can be factored as P( X ) = P( X π ) (2) 0 e bershp functon (a) Hard c-eans clusterng (b) Fuzzy c-eans clusterng Fg. 2. Eaples of hard and fuzzy c-eans algorth

3 3rd WSEAS Internatonal Conference on REMOTE SENSING, Vence, Italy, Noveber 2-23, Fuzzy parttonng s carred out through an teratve optzaton of the obectve functon shown above, wth the update of ebershp u and the cluster centers c by u = c C k= k = c c N u = N u = Ths teraton wll stop when a ( k+ ) ( k ) { } 2 (4) (5) u u < ε (6) where ε s a ternaton crteron between 0 and, whereas k are the teraton steps. Where value. Qr s a quantzaton sze and * s gauss However ths general quantzaton approach can lead undesrable result for actvty recognton n the sart hoe, snce there are unused or useless values, e.g. 4a, at whch there s no actvty ecept sleepng. Ths useless value akes the condtonal probablty table (CPT) sze too large, so the perforance of ths syste would be degraded. To solve ths proble, we quantzed contnuous values wth FCM clusterng to splfy the Bayesan network and CPT sze and to reove the undesrable values. The fuzzy quantzaton process conssts of two steps, clusterng contnuous values and assgnng to dscrete values. In the frst step, we clustered each contnuous value nto c categores wth FCM clusterng algorth. In the second step, we assgn contnuous values to dscrete values wth the cluster nuber, whch are sae as ordered cluster centers. Ths eans that each dscrete value s one of the centers. So f the nuber of cluster s c n whch a contnuous value belongs then the dscrete value s c. In ths paper we used 9 quantzaton of te and 6 quantzaton of user locaton (Fg. 3). 3 Constructng Contet-aware Syste Our approach for user behavor recognton odelng s conssts of two phases: quantzaton by fuzzy clusterng and constructng Bayesan network. The quantzaton process s convertng the contnuous data to the dscrete data, whch s proper for the BN. The constructng BN s coposed of structure learnng and paraeter learnng. 3. Quantzaton by Fuzzy Clusterng In the sart hoe, ost of sensor data are dscrete values lke on/off. However there are soe contnuous values lke te and user poston. To construct Bayesan network easly and effcently, we need to convert a contnuous value to a dscrete value through quantzaton. If we let X be a quantzed value then a general approach to quantzaton can be q X X q = Qr (7) Qr (a) Real poston of user (b) Clustered centers of user postons Fg. 3. User poston converson wth FCM clusterng

4 3rd WSEAS Internatonal Conference on REMOTE SENSING, Vence, Italy, Noveber 2-23, Constructng Bayesan Network We have two assuptons n our odel. The frst one s each actvty could be occurred n the restrcted area. Ths eans that soe actvtes can be occurred near the specfc sensors. The second assupton s we have no nforaton about the dependency between sensors or actvtes. Only dependency between sensors and actvtes ests. Ths assupton splfes the BN structure. Although the splfed structure the perforance s stll good. As we entoned before, the confguraton of the Bayesan networks s conssts of learnng the network structure and paraeters. In the structure learnng, we used utual nforaton to select parent nodes. In the paraeter learnng, we used MAP based EM algorth. Although there are any algorths for structure learnng of BN such as K2, MDL causal dscovery, and CaMML, we used the sple entropy ethod. Accordng to our assupton, our BN structure odel s restrcted as a naïve Bayesan network, whch assues that every node s ndependent ecept ts parent nodes, t s easy to learn the structure. We used utual nforaton for selectng sensor nodes concernng an act to confgure the BN structure. The utual nforaton s defned as [8] I ( Sensor ; Act) = H ( Sensor) H ( Sensor Act) = H ( Sensor) H ( Sensor, Act) H ( Act) p( s, a) = p( s, a)log 2( ) s Sensor a Act p( s) p( a) (8) where H(X) s entropy and H(X Y) s condtonal entropy. to reduce the sze of CPTs. Snce there are ssed data whle collectng saples for learnng, we used MAP based EM algorth for paraeter learnng. The EM algorth s one of paraeter learnng ethod when there are soe ssng data durng collectng the sensor and user actvty data. Fg. 3. The BN structure of proposed odel 4 Sulaton Result In ths secton, the proposed ethod s appled to recognze syste n the vrtual sart hoe. We collected slar user actvtes and sensor data fro the sulator for 0 days (Fg. 4). Forty two rando varables are used to pleent the odel. Twenty one of the rando varables are the state of the sensor nforaton such as te, locaton, lght or TV. Snce the power probles of oble devce, ost sart devces are power connected. The rest of varables represent user actvtes lke sleepng, watchng TV or washng. We perfored sulaton wth 0-fold cross-valdaton to eplore the perforance of the proposed ethod We decded the nuber of connectons accordng to coputer capacty, the aount of saple sze, and robustness. If we ncrease the connecton nuber, then the CPT would be ncreased. Ths ncreased CPT sze requres ore coputer capacty to process and ore saples to learn the BN. Soetes t eans that the BN s overftted losng the robustness. Therefore we need fnd the optal nuber of connectons. Fgure 3 s the eaple of proposed BN structure, whch coposed of L area, M actvtes, and N sensor nodes. The paraeter learnng s updatng the CPTs. As entoned before, we user splfed BN structure

5 3rd WSEAS Internatonal Conference on REMOTE SENSING, Vence, Italy, Noveber 2-23, cofortable nterface between user and hoe. Consderng the dependency between actvtes or sensors would ncrease the perforance. We plan to address ths ssue n future work Acknowledgeent Ths work was supported by the Mnstry of Coerce, ndustry and Energy of Korea (HISP). Fg. 4. The sulator of sart hoe We copared the perforance of proposed ethod wth unforly quantzed case and clustered quantzaton but dfferent structure case. As shown n Fgure 5, the recognton rate of proposed ethod n percent s hgher than other ethods[9]. Fg. 5. User behavor recognton rate 5 Concluson We proposed a user behavor recognton odel to provde ore natural nteracton between resdents and hoe. In the constructng behavor recognton odel, frst t perfors quantzaton by FCM clusterng algorth, and then learn the BN structure based on utual entropy and the BN paraeters by EM algorth. The aor advantage of ths odel s that after regsterng sensor and actvty type, t construct contet-aware odel wthout other odfcaton. Ths eans that we can provde ore References: [] R. Want, A. Hopper, V. Falcãao, and J. Gbbons, The Actve Badge locaton syste, ACM Transactons on Inforaton Systes, Jan. 992, pp [2] E.M. Tapa, S.S. Intlle, and K. Larson, Actvty recognton n the hoe settng usng sple and ubqutous sensors, n Proceedngs of PERVASIVE 2004, Vol. LNCS 300, 2004, pp [3] A. Ranganathan, J. Al-Muhtad, R.H. Capbell, Reasonng about uncertan con- tets n pervasve coputng envronents, IEEE Trans. on Pervasve Coputng, 2004, Vol. 3, No. 2, pp [4] R.E. Neapoltan, Learnng Bayesan Networks, Prentce Hall, 2004 [5] J.C. Bezdek, Pattern Recognton Wth Fuzzy Obectve Functon Algorths, Plenu Press, 98 [6] G. Cooper, E. Hersovts, A Bayesan ethod for the ntroducton of probablstc networks fro data, Machne Learnng, Vol.9, No.4. Oct. 992, pp [7] S.Z. Zhang, Z.N. Zhang, N.H. Yang, J.Y. Zhang, and X.K. Wang, An proved EM algorth for Bayesan networks paraeter learnng, Machne Learnng and Cybernetcs, Vol. 3, Aug. 2004, pp [8] A. Papouls and S.U. Plla, Probablty, Rando Varables and Stochastc Processes, Mc Graw Hll, 2002 [9] W.Y. Chung, J.H. Lee, S.H. Yon, Y.W. Cho, E.T. K, Intellgent Modelng of User Behavor based on FCM Quantzaton for Sart hoe, Journal of Control, Autoaton, and Systes Engneerng, 2007, pp