Knowledge Discovery from Physical Activity

99 Pages·2017·1.4 MB·English

SusanneJauhiainen
Knowledge Discovery from Physical Activity
Master’sThesisinInformationTechnology
May31,2017
UniversityofJyväskylä
DepartmentofMathematicalInformationTechnology
Author: SusanneJauhiainen
Contactinformation: [email protected].fi
Supervisor: TommiKärkkäinenandSamiÄyrämö
Title: KnowledgeDiscoveryfromPhysicalActivity
Työnnimi: KnowledgeDiscoveryfromPhysicalActivity
Project: Master’sThesis
Studyline: Laskennallisettieteet
Pagecount: 99+0
Abstract: Inthismaster’sthesistheKnowledgeDiscoveryinDatabases(KDD)processand
its usage with physical activity data are discussed. The KDD process has multiple steps,
including preprocessing, transformation, and data mining. Clustering is used as the data
mining technique and is introduced in detail. A large set of different Cluster Validation
Indices (CVAIs) and their implementations are tested with the k-means clustering and the
best performing ones further generalized. In the empirical part, physical activity data from
Finnish seventh-grade students is assessed following the KDD process and using multiple
different transformations with different clustering methods. The aim is to find out, whether
unsuperviseddataminingcanhelpdetectnovelandusefulinformationfromthisdata.
Keywords: Knowledgediscovery,physicalactivity,clustervalidationindex
Suomenkielinen tiivistelmä: Tässä pro gradu -tutkielmassa käydään läpi Knowledge Dis-
covery in Databases (KDD) -prosessi ja sen soveltamismahdollisuuksia fyysiseen aktiivisu-
uteenliittyvändatankanssa. KDD-prosessikoostuumonestaerivaiheesta,sisältäenesikäsit-
telyn,datanmuunnoksenjatiedonlouhinnan. Tässätutkielmassatiedonlouhinnanmenetelmänä
käytetään klusterointia, joka käydään läpi yksityiskohtaisesti. Vertailemme myös laajan
joukon eri klusterointi-indeksejä (CVAIs) sekä niiden eri toteutuksia k-means klusteroinnin
kanssa ja esittelemme parhaat näistä yleisemmässä muodossa. Tutkielman empiirisessä os-
assa seitsemäsluokkalaisten koululaisten aktiivisuusdataa tutkitaan KDD-prosessia seuraten
i
ja hyödyntäen monia eri datan muunnoksia ja klusterointimenetelmiä. Tarkoituksena on
selvittää, voiko ohjaamattoman tiedonlouhinnan avulla löytää uutta ja hyödyllistä informaa-
tiotadatasta.
Avainsanat: Knowledgediscovery,fyysinenaktiivisuus,klusterointi-indeksi
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Glossary
KDD KnowledgeDiscoveryfromDatabases
EDA ExploratoryDataAnalysis
LDA LinearDiscriminantAnalysis
SVM SupportVectorMachine
CPM CountsPerMinute
CVAI ClusterValidationIndex
IoT InternetofThings
QS QuantifiedSelf
PCA PrincipalComponentAnalysis
MET MetabolicEquivalentofTask
MVPA ModeratetoVigorousPhysicalActivity
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List of Figures
Figure1.TheoriginalKDD-process. FromFayyad,Piatetsky-Shapiro,andSmyth1996b. 4
Figure2.Withboxplotonecangetaquickoverviewofthedata. Theupperandlower
bounds of the box are the third and first quartile, the line inside the box is the
median,thewhiskersrepresentmaxandminvalues,andtheplussignsstandfor
outliers. ThisboxplotisderivedfromthesedentarydataofstudentsinChapter6. .13
Figure3.Datawithtwoclasses,blackcircleandsquarearetobeclassified................16
Figure4.Fruitclassificationusingdecisiontree..............................................17
Figure 5. On the right LDA with vector w and on the left SVM, where grey markers
are the support vectors and 1 is the margin. Adapted from (Zaki and Meira Jr
(cid:107)w(cid:107)
2014) .....................................................................................17
Figure 6. A line fitted to descripe the relationship between delivery time and delivery
volume. AdaptedfromMontgomery,Peck,andVining2015.........................18
Figure 7. On the left, data drawn from Gaussian distribution and on the right from
Laplaciandistribution. FromÄyrämö2006,pp. 56....................................20
Figure8.Density-baseddatasetwithtwonon-linearlyseparableclusters. Fromhttps://en.wikipedia.org/wiki/DBSCAN.21
Figure 9. The DBSCAN algorithm when minpts=3, where the black circles are core
points, white ones are border points and the black square is noise. The lines
implicateadistancelessthanorequaltoε. ............................................23
Figure10.TheS-datasetswith15clustersandincreasingnoise.............................44
Figure11.Fromlefttoright: S1D2,S2D2,andS5D2........................................45
Figure12.Averagecountsperminuteforhoursoftheweekfromonestudent.............51
Figure13.Availabledatafortheonehourperiods............................................52
Figure14.Availabledataforthehalf-an-hourperiods........................................53
Figure 15. Cluster prototypes with one hour periods considering entire time. Cluster
oneisdarkblue,clustertwoislightblueandclusterthreeisred......................54
Figure16.Clusterprototypesforthemostseparatingtimes..................................55
Figure17.Spatialmedianforeachvariableonthex-axisandthe χ2-valueofKruskal-
Wallis on the y-axis. The line was manually inserted and variables above it are
themostseparating.......................................................................55
Figure 18. Cluster prototypes with half hour periods considering school time. Cluster
oneisdarkblue,clustertwoislightblue,clusterthreeisorangeandclusterfour
isred. .....................................................................................57
Figure19.Clusterprototypesforthemostseparatingtimes..................................58
Figure20.Spatialmedianforeachvariableonthex-axisandthe χ2-valueofKruskal-
Wallis on the y-axis. The line was manually inserted and variables above it are
themostseparating.......................................................................58
Figure 21. Part of activity counts form a student. CPM less than 100 is considered
sedentarytime............................................................................61
Figure 22. CVAIs for transformation 1 that support the number of clusters being 5. In
y-axis the value for CVAI and in x-axis the number of clusters, K. From left to
right the CVAIs are Silhouette, Wemmert Gançarski, and Davies Bouldin. The
proposednumberistheminimumindexvalue. ........................................64
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Figure 23. The results from hierarchical prototype-based clustering. First five cluster
wereformedandthenthetwolargestoneswereclusteredagaintofindsubgroups
(Wartiainen and Kärkkäinen 2015; Saarela and Kärkkäinen 2015). The sizes of
clustersaremarkedonthefigureaswellastheindicesproposingthenumberof
subclustersfound.........................................................................65
Figure24.CVAIsfortransformation2.3thatsupportthenumberofclustersbeing10.
In y-axis the value for CVAI and in x-axis the number of clusters, K. From left
to right the CVAIs are Wemmert Gançarski, Ray Turi and Davies Bouldin. The
proposednumberistheminimumindexvalue. ........................................66
Figure 25. Transformation 1, 10 most separating variables. For example, the most
separatingvariableisasedentaryperiodoflength65minutes,mostlypresentin
cluster11. Thisvariablecanbeconsideredcharacterizingthiscluster,sinceitis
theonemostlyseparatingitfromtheothers............................................67
Figure26.Transformation1,portionofaweekdayinacluster..............................67
Figure 27. Transformation 2.3, 10 most separating variables. For example, the most
separatingvariableisasedentaryperiodoflength31minutes,mostlypresentin
cluster 4. This variable can be considered characterizing this cluster, since it is
theonemostlyseparatingitfromtheothers............................................70
Figure 28. The difference between prototypes and the whole data median. C1 is blue,
C2isgreen,andC3isyellow............................................................72
List of Tables
Table1.Anexampledatabasewithfivetransactionscontainingfiveitems..................14
Table2.NumbersofclusterssuggestedbytheCVAIs .......................................47
Table3.Distributionofthemeasurementdays................................................50
Table4.Variableschosenforclustering.......................................................53
Table 5. Metadata for clusters with one hour periods considering entire time. Total
activityisthesumofallactivityovertheweek.........................................56
Table 6. Metadata for clusters with half-hour periods considering school time. Total
activityisthesumofallactivityovertheweek.........................................59
Table7.Exampletableofsedentaryperiodsofthestudents. ................................62
Table8.Metadataforclustersobtainedwithtransformation1...............................68
Table9.Metadataforclustersobtainedwithtransformation2.3.............................69
Table10.Metadataforclusters. ...............................................................71
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Contents
1 INTRODUCTION ....................................................................... 1
2 THEKDDPROCESS ................................................................... 4
2.1 Datamatrix ........................................................................ 5
2.2 Preprocessing...................................................................... 6
2.3 Transformation .................................................................... 7
2.3.1 Dimensionreduction ....................................................... 8
2.4 Datamining........................................................................10
2.4.1 Exploratorydataanalysis...................................................12
2.4.2 Frequentpatternmining....................................................13
2.4.3 Classification................................................................15
2.4.4 Regression...................................................................18
2.5 Summary...........................................................................19
3 CLUSTERING...........................................................................20
3.1 Whatisacluster...................................................................20
3.2 Hierarchicalclusteringmethods..................................................22
3.3 Density-basedclusteringmethods................................................22
3.4 Prototype-basedclusteringmethods..............................................23
3.4.1 Clusterinitialization ........................................................24
3.5 Robustclustering..................................................................25
3.6 Clustervalidation..................................................................25
3.7 Summary...........................................................................30
4 MEASURINGANDQUANTIFYINGPHYSICALACTIVITY .....................32
4.1 Accelerometers....................................................................33
4.1.1 ActiGraphandcounts ......................................................35
4.2 Gyroscopes ........................................................................35
4.3 Pedometers ........................................................................36
4.4 Heartratemonitors................................................................36
4.5 Visionsensors .....................................................................36
4.6 GlobalPositioningSystem........................................................37
4.7 Physicalactivitydataapplications................................................37
4.7.1 Humanactivityrecognition ................................................38
4.7.2 Gaitanalysis ................................................................39
4.7.3 Monitoringandmedicaldiagnosis.........................................40
4.7.4 Activityandsports..........................................................40
4.8 Activitymonitors..................................................................41
4.8.1 Consumer-basedphysicalactivitymonitors...............................42
4.8.2 Validityofconsumer-basedphysicalactivitymonitors...................42
4.9 Summary...........................................................................43
5 CVAITESTS.............................................................................44
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5.1 Testdatas ..........................................................................44
5.2 Methods............................................................................45
5.3 Results .............................................................................45
5.4 Conclusion.........................................................................46
6 KNOWLEDGEDISCOVERYFROMTHEACTIVITYOFFINNISHSCHOOL
CHILDREN..............................................................................49
6.1 RecommendationsandtheFinnishschoolsonthemoveprogram..............49
6.2 Studypopulationanddatacollecting.............................................50
6.3 Preprocessing......................................................................51
6.4 Weeklyphysicalactivityofthestudents .........................................51
6.4.1 Transformation..............................................................51
6.4.2 Datamining.................................................................53
6.4.3 Results.......................................................................54
6.4.4 Entiretimewithonehourperiods .........................................54
6.4.5 Schooltimewithhalf-an-hourperiods ....................................57
6.4.6 Conclusion ..................................................................60
6.5 Sedentarybehaviourofthestudents..............................................60
6.5.1 Transformation..............................................................60
6.5.2 Datamining.................................................................64
6.5.3 Results.......................................................................66
6.5.4 Transformation1............................................................66
6.5.5 Transformation2.3..........................................................69
6.6 Sedentarybehaviourofthestudents2............................................70
6.6.1 Transformation..............................................................70
6.6.2 DataMining.................................................................71
6.6.3 Results.......................................................................71
6.7 Summary...........................................................................73
7 CONCLUSION ..........................................................................75
BIBLIOGRAPHY..............................................................................77
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1 Introduction
Physical activity can be defined as "any bodily movement produced by skeletal muscles
that requires energy expenditure" (Caspersen, Powell, and Christenson 1985). The amount
and form of daily physical activity, over the whole life span, can greatly affect individuals
health and quality of life, which makes the assessment of it very important. Active lifestyle
can, for example, reduce the risk of coronary heart disease, hypertension, diabetes, some
cancers, and premature mortality in general (Health and Services 1996). According to the
World Health Organization (WHO), physical inactivity is the fourth biggest global risk for
mortality,responsiblefor5.5%-over3million-ofalldeaths(WHO2009).
As the technical abilities to measure activity’s different components and recognize its dif-
ferent forms have advanced, many applications and studies in a variety of domains have
emerged. For example in gerontology, monitoring the activity of elderly people can be used
todetectiftheyfall(Mubashir,Shao,andSeed2013)oriftheyhavechestpainorheadache
(KhanandSohn2011). Thisnondiscruptivemonitoringcanbedonefromoutsidetheirfree-
living conditions, which improves the quality of their lifes and saves elderly care resources
by supporting home care. In sports the foot-ground contact time and the stride ratecadence
can be analysed when running and utilized in maximizing the performance (Morin et al.
2007;Weyandetal.2000). Heartratemonitoringhasalsobecomeverygeneralandcangive
alotofinformationabout,forexample,theintensityofphysicalactivity(Vesterinen2016).
There are many different components of human activity that can be measured with a variety
of devices such as accelerometers or heart rate monitors. For example, the total physical
activity, the duration, frequency and intensity of physical activity, energy expenditure, as
well as number of steps, speed and distance when walking (Butte, Ekelund, and Westerterp
2012) can be determined from the raw sensory measurements. Furthermore, the locomotive
activities (e.g. walking, jogging, running) can be classified into their own categories (Bao
andIntille2004;Kwapisz,Weiss,andMoore2011).
Measuringofthesecomponentsofhumanactivitycanbedoneobjectivelywithmanysensors
which can be divided into external and wearable ones (Lara and Labrador 2013). Firstly,
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inertial sensors are wearable sensors based on inertia, with accelerometers being the most
widely used example (Avci et al. 2010). Acceleration data can be used to estimate, e.g., the
intensityofphysicalactivityovertime(ChenandBassett2005).
Secondly, sensors for physiological signals can be used in measuring the activity. These
are normally wearable sensors. Heart rate monitors, for instance, have developed rapidly in
recent years and are today very common in sports and training. They are mainly used to
determinetheexerciseintensity(AchtenandJeukendrup2003),butheartratevariabilitycan
also be used to analyze how well an athlete is adapting to endurance training (Vesterinen
et al. 2013) or in prevention and detection of overtraining (Achten and Jeukendrup 2003).
Thirdly, vision sensors are external sensors used, for example, in gait analysis and other
machinevisionapplications(Poppe2007).
With the increase of different human activity sensors and monitors, massive streams of data
about our lives are being created continously. This has led to suggestion of the concept
InternetofHumans(Arbiaetal.2015),inconnectionwiththeInternetofThings(IoT).While
IoT is the general term referring to the things and devices being connected to the internet,
IoH can be thought as humans being connected to internet by using different monitors and
loading data about themselves to the internet. This personal data can also be referred to as
MyData(PoikolaandHonko2010). Anemergingtrendistotalkaboutsocalledquantified-
self (QS), which has been definded "as any individual engaged in the self-tracking of any
kindofbiological,physical,behavioral,orenvironmentalinformation"(Swan2013). Asthe
amountofdatagrows,newapproachesareneededtoutilizeitmoreeffectively.
Knowledge Discovery in Databases (KDD) is a process of finding valid and novel informa-
tionfromlargeamountsofdata(Fayyad,Piatetsky-Shapiro,andSmyth1996b). Ithasmany
steps, beginning from preprocessing the data, transforming it and finally by using data min-
ing techniques in order to find patterns that can be interpreted into knowledge. Compared
to the more traditional analysis techniques, data mining is rather about finding novel and
unexpected information from the data than about confirmation of some existing hypothe-
ses (Hand, Mannila, and Smyth 2001). KDD’s potential to utilize the large datasets, that are
nowcommoninthefieldofphysicalactivity,makesitanaturalframeworkfortheknowledge
discoveryfromsuchsources.
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