[IEEE 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) - Seattle, WA, USA (2011.03.21-2011.03.25)] 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) - An approach to Collaborative Context Prediction

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An Approach to Collaborative Context PredictionChristian Voigtmann, Sian Lun Lau, Klaus DavidDepartment of Computer ScienceUniversity of Kassel, Germany{voigtmann, slau, david}@uni-kassel.deAbstractContext prediction approaches forecast future con-texts based on known context patterns to adapt e.g., services inadvance. In the case of the users context history not providingsuitable context information for the observed context pattern, tothe best of our knowledge context prediction algorithms will failto forecast the appropriate future context. To overcome the gapof missing context information in the users context history, wepropose the Collaborative Context Prediction (CCP) approach.CCP utilises the collaborative characteristics of existing recom-mendation systems of social networks. To evaluate the CCPmethod an experimental comparison of the proposed methodagainst the local Alignment context predictor is carried out.Index Termscontext prediction, context awareness, tensordecomposition, hosvd, collaborative.I. INTRODCTIONOne interesting research issue in the field of context-awaresystems and environments is context prediction. Based on theavailable context data, such systems and environments predictfuture context of the user. With the help of this predictedinformation, users in ubiquitous environments can be assistedto a greater extend in different ways. Taking for example, aresearch assistant who presents the progress of his work inthe same room every week. Before he enters the room forhis next presentation, the context-aware system automaticallyadapts the designated services in the room to be ready forhis presentation using a prediction system spanning the wholeuniversity. Possible context information useful for the predic-tion are his movement patterns or devices he has used in thepast. A common approach to enable the prediction of futurecontext is to make use of the gathered and stored contextsrelated to the users actions or to the users environment. Theinformation is needed by a context prediction algorithm toforecast contexts for a given context pattern. However, if theresearch assistant gives his presentation in a room he has notbeen in before, his current movement patterns are unknownto the context prediction system. Current context predictionapproaches that rely on the context history of a single userfail to forecast the users next context - doing a presentation.Therefore, the adaptations would not take place.To overcome the problem of missing context information ina single users context history, we utilise a collaboration-basedtechnique. We propose including and utilising the contexthistories from other users to solve the problem of missingcontext patterns in a users context history. We call this ideaCollaboration-based Context Prediction (CCP). To enrich thecontext history of the user, we use the Higher Order SingularValue Decomposition (HOSVD) method that has been appliedsuccessfully in the field of tag recommendation [1]. Withregard to the above-mentioned scenario, the future context ofthe research student - giving a presentation - which is based onunknown movement patterns could be predicted using CCP, ifthe context is provided by context histories of his colleaguesand if these histories show sufficient similarities to the researchstudents history.Existing state of the art to context prediction is outlined inthe next Section. An introduction to the Collaboration-basedContext Predictor and to the underlying HOSVD technique isoutlined in Section III. Section IV gives a detailed exampleto illustrate how the approach works. A proof of conceptregarding accuracy is given in Section V, by comparing theCCP approach to an Alignment predictor which is based on theSmith-Waterman-Algorithm. Section VI concludes the paper.II. RELATED WORKIn this section we present existing state of the art contextprediction approaches briefly. One of the first projects that in-cludes a mechanism to forecast future contexts is the NeuronalNetwork House project directed by Michael C. Mozer [2]. Inthis project, lifestyle information of occupants was gatheredto predict their next actions. Another smart home projectconducted by Diana J. Cook is called MavHome [3]. In thecase of this project the Active LeZi approach [4], a sequence-matching algorithm, and the Episode Discovery algorithm [5]have been developed to forecast, among others, devices theinhabitants will be interacting next with. The authors in [6]suggest a method to predict a users next location by usingand interpreting his or her GPS data. The introduced approachclusters the gained GPS data to meaningful locations and usesa Markov model for the prediction process.A context time series prediction algorithm that is based onlocal alignment techniques is introduced in [7]. This approachis inspired by algorithms with a focus on computationalbiology. On the one hand, the author evaluated his proposedcontext prediction method by applying Alignment to a windpower dataset to predict the wind power required in the future.On the other hand, a dataset consisting of the information onthe location of a users mobile phone has been used to predictthe users location trajectory.In [8] a Structured Context Prediction algorithm is pre-sented. This approach tries to overcome the trade-off forcontext prediction methods needing to be generic with beingefficient by using additional knowledge about the applicationdomain given by the developer at the time of design. The used8th IEEE Workshop on Context Modeling and Reasoning978-1-61284-937-9/11/$26.00 2011 IEEE 438evaluation approach focuses on the availability prediction oftwo services. The mentioned approaches only use the contexthistory of the user whose future context should be predictedand do not explicitly make use of existing relations of userscontext histories. Thus, the fact to use context histories of otherusers to gain extra information for the forecasting process hasnot been considered so far. In the next section our proposedapproach is described in more detail.III. COLLABORATIVE-BASED CONTEXT PREDICTIONWe propose the Collaborative-based Context Prediction(CCP) approach that increases the possibility to make acurrently unknown context pattern of a user available for fore-casting the next future context. The Collaborative UbiquitousEnvironment presented in Figure 1 forms the foundation forthe CCP approach. The environment consists of three differententities. The first entity is represented by the set of users U U of the Collaborative Ubiquitous Environment, the secondby the set of possible context patterns Cp CP and the thirdby the set of predictable future contexts Fc FC. Thereforethe history of a user Ui is described by Hi CP FC.U1U2U3UnCp |Fc2 1 Cp |Fc5 9Cp |Fc7 5 Cp |Fc8 j Cp |Fc3 2Cp |Fc1 n Cp |Fcm-1 iCp |Fcm 1Context HistoryUserCollaborative Ubiquitous EnvironmentCp |Fc2 1Cp |Fcm 1Cp |Fcm-1 iCp |Fc5 9Cp |Fc8 jCp |Fc8 jCp |Fcm-1 iCp |Fcm-1 iFig. 1. Presents n users of a Collaborative Ubiquitous Environment with ndifferent context histories. Equal context parts are marked in the same colour.Every context part in the context history Hi of the user Ui consists of twoelements. Cp CP indicates the context pattern and Fc FC indicates thefuture context that follows the previous context pattern.HOSVD is used to enrich the context history of the user withadditional latent information by using existing relations (equalcontext parts) between the context histories of the users in theCollaborative Ubiquitous Environment (see Figure 1). Latentinformation comprises new context parts in the context historyof the user that were formally unknown and can be used toinfer the next future context. The basic idea of HOSVD is torestrict the dimensionality of each entity of a CollaborativeUbiquitous Environment to a specific size where each entityonly contains relevant, less noisy information by using then-mode product [9]. Afterwards, the downsized informationspace is used to recalculate the Collaborative UbiquitousEnvironment, based on the most relevant information usingthe n-mode product again.U1U2U3UnCp1Cp2Cp3 CpmFc1Fc2Fcj111111Fig. 2. 3-order tensor to store data of a Collaborative Ubiquitous Envi-ronment. The first dimension characterises all users U of the CollaborativeUbiquitous Environment that provide information in the form of context data.The second dimension represents all context patterns CP that are availablein the context histories of the users. FC symbolises the third dimension, thefuture contexts, that can be inferred as the next possible context of a userU U for a given context pattern.c1c2c3Fig. 3. Downsized 3-order tensor structure that contains only the mostrelevant data of every dimension.To demonstrate the practical use of HOSVD we apply thistechnique to the presented Ubiquitous Collaborative Environ-ment. For the storage of the data we use a 3-order tensorA |U||CP||FC|, compare Figure 2. The existing relationsbetween the entities U , CP, FC are stored in the tensorstructure A with a one. All relations that do not exist aretreated as values of zero.To minimize the number of unknown context pattern inthe users history we downsize the dimensionality sizes ofthe 3-order tensor structure A. As a result, we receive a3-order tensor c1c2c3 whose three dimensions arereduced to the information that spans the space that containsthe most relevant information. The size of the dimension Uis reduced to c1, the size of the dimension CP is reducedto c2 and the third dimension FC is collapsed to the sizeof c3. Figure 3 shows the tensor - marked in red - with itsreduced dimensionality size. symbolises the approximationof the tensorA. Afterwards HOSVD is used to retransform thetensor to the initial size of the dimension of the tensor A byreusing the n-mode product as described above. The resultingtensorA |U||CP||FC| finally concludes new informationin terms of new relations between the three dimensions thatcan be used to forecast the users next contexts.IV. ILLUSTRATIVE EXAMPLEIn order to show how our approach works, the CollaborativeContext Predictor is applied to an illustrative example. Thisexample presupposes three users of a Collaborative UbiquitousEnvironment. Figure 4 presents the three context histories ofthe users. Equal context parts are marked with the same color.In contrast to the used datasets in our experimental section,439in this example context patterns are not mapped to multiplefuture contexts. The size of the context parts is determinedto an equal window size of four. Therefore every context partconsists of three contexts and one future context marked inbold. All in all there are five different context parts composingof ten different contexts.U1U2U3 BBCY BBDZ BCCY BBDZ EFGZ BBDZ EFGZ EFGZFFXY FFXY BBDZ BBDZABCX ABCX BCCY ABCX BCCY EFGZ EFGZUser1 2 3 4 5 6 7 8EFGZBBDZ BBDZ FFXY FFXYContextPartsFig. 4. Sample dataset consisting of context histories of three users.As we can see, the context history of U1 does not provideinformation for the context pattern {FFX, BBD}, the historyof U2 does not provide information for the context pattern{ABC, BCC, EFG} and in the history of U3 the patterns{ABC, FFX} havent been stored yet. Hence it is not possible,for example, to provide a prediction for the next sensedcontext pattern for U1, if this context pattern is either FFXor BBD. The approach takes advantage of direct and indirectrelations between the histories of the users. Direct relationsare characterised by equal context parts between two users.Indirect relations between two users e.g., U1 and U2 exist, ifthe following two conditions are fulfilled: U1 and U2 do not have the same context parts, U3 features similarities of both U1 and U2.CCP first transforms the underlying Collaborative Ubiq-uitous Environment to a 3-order tensor structure with sizeA 353. (cf., Figure 5). Then HOSVD for tensordimensionality reduction is applied by reducing the dimensionsize of the user to one. The resulting core tensor structureis 153. By reapplying HOSVD to the core tensorstructure containing the approximated data the tensor structurepresented in Figure 6 containing new information is received.The resulted tensor contains new relations between users,context patterns and future contexts which are symbolised bythe new values. Because context patterns are not mapped tomultiple future contexts in this example the relations betweenusers, context patterns and future context in the resulted tensorA are obvious. Otherwise, the future context with the highestvalue that forms a relation to a given user and context patternwill be predicted. For predicting future contexts for contextpattern FFX or BBD to U1, we can now use the new relationsprovided by the resulted tensor to make a prediction. Forthe pattern FFX we will predict Y and for BBD we willpredict the future context Z. The resulting prediction for BBDis reasonable, because U1 shares the same context patterns{EFG, BCC} with U3. The same applies to the predictionfor pattern FFX, because U1 has a direct relation to U3 andU3 has a direct relation to U2 so U1 and U2 hold indirectsimilarities. This illustrative example shows that CCP can beused to provide further information to context histories of users3224 343U1U2U3XYZABCBCCFFXBBDEFGABCBCCFFXBBDEFGABCBCCFFXBBDEFG3Fig. 5. Shows the tensor A 353 that contains the information ofthe used sample data. The tensor consists of three users, five different contextpatterns and three different future contexts.1.31.8U1U2U3XYZABCBCCFFXBBDEFGABCBCCFFXBBDEFGABCBCCFFXBBDEFG0. 6. Presents the resulted tensorA 353 that contains new relationsbetween users, context patterns and future contexts.by using existing direct or indirect relations to context historiesof other users. In the next section we will verify this resultby applying the CCP approach to a real dataset consisting ofmovement patterns.V. EXPERIMENTAL RESULTSIn this section we present experimental results of our CCPapproach that is based on real-world context data. We showthat context histories of additional users can be utilised toallow context prediction even if the user himself, in thiscase the research assistant in our introduction, does not havesuitable information in his history. Furthermore, we explorehow the prediction accuracy for so far unobserved contextpatterns is affected by the number of used histories. Finally,we compare the accuracy of our results to a local Alignmentcontext prediction approach that has access to the same contexthistory database using the Smith-Waterman-Algorithm [10].A. DatasetThe dataset contains acceleration data recorded using theaccelerometer in a smartphone annotated with the movementsperformed by test users. These were carried out in a way sim-ilar to the experiments performed in our previous work [11].Smartphones used for the recording of acceleration includeda Nokia N95 8GB, a 5730 Xpressmusic and a N900. For thepurpose of this work, the Symbolic Aggregate approXimation(SAX) representation of the time series proposed by Lin et. al[12] is used to transform the magnitude of the raw accelerationdata into a series of alphabetical symbols.Each time series was normalized and split into windowsof 4 seconds. First, each window was transformed into apiecewise aggregate approximation (PAA) representation of440GoDownstairs - baecdbbbeedfebceeaceebabfeaceeda - B1GoDownstairs - cdcccccccbcdfaacddefbbcefacdcddb - B0...GoDownstairs - bacbfbabddebabbfdabdfdcabbfabcfe - B2GoUpstairs - ebebbcefadebbdfeafebdfcaeccccddb - C1...GoUpstairs - ccdcdccbcefdcabfccbcfddaafdccbfd - C1GoUpstairs - eafbbbceccbbfddbcdeabfcbfaaedccb - C2......Fig. 7. Illustration of acceleration data entries of a test dataset and their approximation.the given normalized time series, then converted into symbolstrings with the distance matrix defined in Lins work. Weapplied the SAX representation using string lengths of 32 anda total of 6 symbols {a, b, c, d, e, f}. This enables the use ofa simple clustering approach where each movement patternis divided into maximum three sub clusters. Sub clustersof go downstairs are represented by {B0, B1, B2}, subclusters of go upstairs are represented by {C0, C1, C2}, etc.Henceforth, a maximum of 15 resulting different clusters areused to represent the context information in the history of theusers. Figure 7 illustrates two time series each with a windowsize of 4 seconds. Below the two acceleration curves a snippetof a used test dataset is presented. The data snippet illustratesthe label, the SAX transformation of the acceleration data andthe label of the respective sub cluster.For our experiments we used four context histories ofdifferent users. Every context history containing approximately1000 sub clustered and time-ordered contexts resulted fromthe recorded movement behaviour of the users. To gain arepresentative number of different evaluation sets, we useddifferent window sizes for the length of the context parts {3,5, 7} to divide the four existing context histories. Moreover,we generated additional evaluation sets by removing contextparts that occurred successively. Datasets without successivecontext parts are called single-mode datasets, datasets withsuccessive context parts are called all-mode datasets.B. Experimental ConfigurationFor the evaluation we apply the CCP and the local Align-ment approach to different test datasets. A test dataset con-sists of intersections of exactly two users context histories.Intersections are context parts that occur in both histories.If both users have ten equal context parts in their histories,for example, the test dataset contains twenty intersectionsentries. 10 intersections from the perspective of the first userto the second user and 10 intersections from the reverseperspective. The test datasets are used to simulate missingcontext information in the context history of the correspondingusers. For every test dataset three training datasets exist thatare used to construct the prediction models for the appliedcontext prediction approaches.The first training dataset contains the information of thecontext histories on the two users the test dataset has beengenerated from. The second training dataset extends the in-formation of the first training dataset by adding the contexthistory information of a third user. The third dataset extendsthe second by adding the history information of a fourthuser. For the prediction process we use the leave-one-outstrategy for both context prediction approaches. In doingso, each single context part of the test dataset is removedtemporally one after the other from the context history of thecorresponding user in the current training dataset.Every time a single context part is removed temporally, theprediction model is constructed anew with the reduced trainingdataset and is used to forecast the next context of the contextpattern given by the current context part of the test dataset.Finally, the predicted context is compared to the actual futurecontext that follows the context pattern.C. ResultsIn this subsection we provide the results gained after ap-plying the CCP and the local Alignment approach to differenttest datasets. Altogether we generated 24 test datasets. The testdatasets result from four different context history combinationsof users, combined with three different window sizes forthe context parts and the two dataset modes. Each chartpresented in the Figures 8 to 11 shows four histogram barsfor every window size that indicate the different contextprediction accuracies. The blue bars indicate the accuracy ofthe CCP approach for the different training datasets. The redbar indicates the accuracy of the local Alignment approach.We show only one result for the local Alignment approachfor every window size because local Alignment always leadsto the same accuracy results in our experiments despite of441TABLE ITENSOR DIMENSIONALITY FOR THE DIFFERENT WINDOW SIZES OF THE FOUR TEST DATASETS. REMARK: THE TENSOR DIMENSIONALITY FOR ALL ANDSINGLE DATA MODE IS SIMILAR.Wnd = 3 Wnd = 5 Wnd = 7A1A2A3 A1A2A3 A1A2A3Fig. 8 2x44x11 3x63x11 4x68x11 54 2x104x11 3x136x11 4x183x11 34 2x123x11 3x184x11 4x250x11 18Fig. 9 2x53x11 3x64x11 4x66x11 48 2x130x10 3x180x10 4x232x10 32 2x142x10 3x209x10 4x275x10 12Fig. 10 2x54x11 3x59x11 4x67x11 44 2x125x11 3x160x11 4x205x11 28 2x125x11 3x176x11 4x239x11 10Fig. 11 2x61x11 3x68x11 4x75x11 68 2x143x10 3x188x10 4x232x10 38 2x153x11 3x221x11 4x277x11 16using different trainings datasets. This indicates that the localAlignment approach already gets most of the information ofthe two context histories the test dataset has been generatedfrom and does not benefit from additional context histories.Table I presents the different sizes of the tensor modelscreated from the training datasets. A1indicates the tensorcontaining context histories of two users, etc. Furthermore, theTable displays the size of the test datasets for the differentwindow sizes. To construct the approximated prediction mod-els from the tensors containing the different training datasets,in each case only the user dimension has been reduced to thesize of one. The accuracy of the context prediction process getsworse dramatically, if the context patterns dimension or thefuture contexts dimension of the tensors presented in Table Ihas been reduced.The results indicate two important cognitions. Firstly, theresults show that it is possible to forecast future contexts basedon an unknown context pattern by using additional contexthistories of other users. Secondly, in most cases the predictionresults of the CCP approach can be improved by using morethan just one additional context history. The division of thecontext histories in context parts with different window sizesshow that the prediction results for smaller window sizesnormally achieve less accuracy than for higher window sizes.That is because the smaller the chosen window size the higherthe number of entries in the test dataset. In the comparisonof the single-mode datasets and the all-mode datasets werecognise that in most cases the prediction for the all-modedatasets achieved better results. Furthermore, CCP providesmore accurate prediction results than the local Alignmentapproach for all examined test datasets.VI. CONCLUSIONSIn this paper we introduced the Collaborative ContextPredictor (CCP) method. This approach tries to overcomethe problem of unknown or missing context information ina single users context history by using existing relationsbetween context histories of several users. Experiments onreal-world movement data showed that CCP is able to obtainquite accurate prediction results, even if the underlying contextinformation is missing in the context history of the respectiveuser. Furthermore our experiments have shown that CCPobtains better results than the local Alignment predictor forevery test dataset. Next, we intend to add fuzziness to theCCP approach for which context patterns in the histories ofthe users do not have to match exactly to be considered asexisting relations.ACKNOWLEDGMENTThe authors are involved in the VENUS research project.VENUS is a research cluster at the interdisciplinary Re-search Center for Information System Design (ITeG) at KasselUniversity. We thank Hesses Ministry of Higher Education,Research, and the Arts for funding the project as part ofthe research funding program LOEWE - Landes-Offensivezur Entwicklung Wissenschaftlich-okonomischer Exzellenz.For further information, please visit: http://www.iteg.uni-kassel.de/venus.The authors would like to acknowledge the German FederalMinistry of Education and Research (BMBF) for funding theproject MATRIX (Forderkennzeichen 01BS0802). The authorsare responsible for the content of the publication.REFERENCES[1] P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos, Tag recom-mendations based on tensor dimensionality reduction, in RecSys 08:Proceedings of the 2008 ACM conference on Recommender systems.New York, NY, USA: ACM, 2008, pp. 4350.[2] M. C. Mozer, The neural network home: An environment that adaptsto its inhabitants, in In AAAI Spring Symposium on Intelligent Environ-ments, 1998, pp. 110114.[3] D. C. Michael, M. Youngblood, E. O. Heierman, K. Gopalratnam,S. Rao, A. Litvin, and F. Khawaja, Mavhome: An agent-based smarthome, 2003.[4] K. Gopalratnam and D. J. Cook, Active lezi: An incremental parsing al-gorithm for sequential prediction, in In Sixteenth International FloridaArtificial Intelligence Research Society Conference, 2003, pp. 3842.[5] E. O. Heierman, III and D. J. Cook, Improving home automationby discovering regularly occurring device usage patterns, in ICDM03: Proceedings of the Third IEEE International Conference on DataMining. Washington, DC, USA: IEEE Computer Society, 2003, p. 537.[6] D. Ashbrook and T. Starner, Using gps to learn significant locationsand predict movement across multiple users, 2003.[7] S. Sigg, S. Haseloff, and K. David, An alignment approach for contextprediction tasks in ubicomp environments, IEEE Pervasive Computing,vol. 99, no. PrePrints, 2010.[8] M. Meiners, S. Zaplata, and W. Lamersdorf, Structured context pre-diction: A generic approach, in Proceedings of the 10th IFIP In-ternational Conference on Distributed Applications and InteroperableSystems (DAIS 2010). Springer, 6 2010, pp. 8497.[9] T. G. Kolda and B. W. Bader, Tensor decompositions and applications,SIAM Review, vol. 51, no. 3, pp. 455500, September 2009.[10] T. F. Smith and M. S. Waterman, Identification of common molecularsubsequences. J Mol Biol, vol. 147, no. 1, pp. 195197, March 1981.[Online]. Available: http://view.ncbi.nlm.nih.gov/pubmed/7265238[11] S. L. Lau, I. Konig, K. David, B. Parandian, C. Carius-Dussel, andM. Schultz, Supporting patient monitoring using activity recognitionwith a smartphone, in The Seventh International Symposium on WirelessCommunication Systems (ISWCS10), 2010.[12] J. Lin, E. Keogh, L. Wei, and S. Lonardi, Experiencing sax: a novelsymbolic representation of time series, Data Mining and KnowledgeDiscovery, vol. 15, no. 2, pp. 107144, October 2007. [Online].Available: http://dx.doi.org/10.1007/s10618-007-0064-z442 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 1 and user 2 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 1 and user 2 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 1 and user 2 (all data)2User(CCP)3User(CCP)4User(CCP)AlignmentFig. 8. Test datasets generated from the intersections of user 1 and user 2. Used with different window sizes and data modes. 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 3 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 3 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 3 (all data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 3 (all data)2User(CCP)3User(CCP)4User(CCP)AlignmentFig. 9. Test datasets generated from the intersections of user 2 and user 3. Used with different window sizes and data modes. 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 1 and user 3 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 1 and user 3 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 1 and user 3 (all data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 1 and user 3 (all data)2User(CCP)3User(CCP)4User(CCP)AlignmentFig. 10. Test datasets generated from the intersections of user 1 and user 3. Used with different window sizes and data modes. 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 4 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 4 (single data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 4 (all data)2User(CCP)3User(CCP)4User(CCP)Alignment 0 20 40 60 80 100Wnd=3 Wnd=5 Wnd=7Accuracy in %Window SizesBased on test dataset of user 2 and user 4 (all data)2User(CCP)3User(CCP)4User(CCP)AlignmentFig. 11. Test datasets generated from the intersections of user 2 and user 4. Used with different window sizes and data modes.443


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