Daily Happiness Recognition from Mobile Phone Data

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Daily Happiness Recognition from Mobile Phone Data

  1. 1. Happiness Recognitionfrom Mobile Phone DataAndrey Bogomolov1 Bruno Lepri2 Fabio Pianesi21University of Trento,Via Sommarive, 5I-38123 Povo - Trento, Italy2Fondazione Bruno KesslerVia Sommarive, 18I-38050 Povo - Trento, ItalyEmoPAR group meeting 2013-JUN-19, Trento, Italy.1 / 49
  2. 2. OutlineIntroductionProblem StatementSource DataRecognition ModelBasic FeaturesFinal Feature SpaceResultsLimitationsSummary2 / 49
  3. 3. Happiness as an emotional state – why is it important?Your ideas?. . .3 / 49
  4. 4. General Problem StatementInputsPervasive technology data.Multimodal data.OutputsEmotion recognition.Mood recognition.Personality recognition.4 / 49
  5. 5. Our Problem StatementInputsSmartphone call log.Smartphone sms log.Smartphone Bluetooth proximity hits.OutputsDaily happiness recognition.3-classes: {not happy, neutral, happy}.5 / 49
  6. 6. Data Collection117 subjectsdates: 21 February, 2010 – 16 July, 2011Source Space Dataset: Living Laboratoryphone calls 33497sms 22587Bluetooth hits 14609396 / 49
  7. 7. Happiness DataRecorded Happiness Scores Density0.00.20.40.60.80 2 4 6ScoreDensity7 / 49
  8. 8. Happiness DataDescriptive Statisticsnumber of records 12991mean 4.84standard deviation 1.26median 5.00mean average deviation 1.48min 1.00max 7.00range 6.00skew -0.39kurtosis -0.078 / 49
  9. 9. Happiness DataWithin-person and between-subject variance0 1 2 3 4 5 60.00.20.40.60.81.01.21.4density.default(x = r1[, 1])VarianceDensityWithin−Person VarianceBetween−Person Variance9 / 49
  10. 10. Feature SpaceBasic FeaturesGeneral Phone UsageDiversityActive BehaviorsRegularity10 / 49
  11. 11. Feature SpaceBasic Features: General Phone Usage1. Total Number of Calls (Outgoing+Incoming)2. Total Number of Incoming Calls3. Total Number of Outgoing Calls4. Total Number of Missed Calls5. Number of SMS received6. Number of SMS sent11 / 49
  12. 12. Feature SpaceBasic Features: Diversity7. Number of Unique Contacts Called8. Number of Unique Contacts who Called9. Number of Unique Contacts Communicated with (Incoming+Outgoing)10. Number of Unique Contacts Associated with Missed Calls11. Entropy of Call Contacts12. Call Contacts to Interactions Ratio13. Number of Unique Contacts SMS received from14. Number of Unique Contacts SMS sent to15. Entropy of SMS Contacts16. Sms Contacts to Interactions Ratio12 / 49
  13. 13. Feature SpaceBasic Features: Active Behaviors17. Percent Call During the Night18. Percent Call Initiated19. Sms response rate20. Sms response latency21. Percent SMS Initiated13 / 49
  14. 14. Feature SpaceBasic Features: Regularity22. Average Inter-event Time for Calls (time elapsed between two events)23. Average Inter-event Time for SMS (time elapsed between two events)24. Variance Inter-event Time for Calls (time elapsed between two events)25. Variance Inter-event Time for SMS (time elapsed between two events)14 / 49
  15. 15. Feature SpaceProximity FeaturesGeneral Bluetooth Proximity1. Number of Bluetooth IDs2. Times most common Bluetooth ID is seen3. Bluetooth IDs accounting for n% of IDs seen4. Bluetooth IDs seen for more than k time slots5. Time interval for which a Bluetooth ID is seen6. Entropy of Bluetooth contactsDiversity7. Contacts to interactions ratioRegularity8. Average Bluetooth interactions inter-event time(time elapsed between two events)9. Variance of the Bluetooth interactions inter-event time(time elapsed between two events)15 / 49
  16. 16. Feature Space Innovation“Background Noise” Featuresa) peoples activity, as detected through their smartphonesb) the weather conditions {humidity, wind speed, pressure,total precipitation and visibility}c) personality traits {“Big Five"}Functional Innovationa) time domain – sliding window functionsb) Miller-Madow correction for entropy calculationˆHMM(θ) ≡ −pi=1θML,i log θML,i +ˆm − 12N16 / 49
  17. 17. Top-30 Features-1 0 1 MeanDecreaseAccuracy MeanDecreaseGinimeanTemperature 0.0099 0.0033 0.0094 0.0082 154.8379humidity 0.0047 -0.0033 0.0115 0.0074 149.7668pressure -0.0002 0.0008 0.0015 0.0011 149.0302windSpeed 0.0028 0.0017 0.0051 0.0040 142.7727visibility 0.0024 -0.0009 0.0051 0.0034 120.8683neuroticism 0.0690 0.0288 0.0399 0.0419 90.5721conscientiousness 0.0659 0.0480 0.0668 0.0627 90.2708extraversion 0.0511 0.0357 0.0472 0.0454 76.9467openness 0.0656 0.0340 0.0406 0.0429 73.9181totalPrecipitation 0.0007 -0.0000 0.0012 0.0009 73.5273agreeableness 0.0536 0.0282 0.0235 0.0289 70.7261bluetoothQ95TimeForWhichIdSeen 0.0233 0.0120 0.0149 0.0155 22.4018bluetoothQ90TimeForWhichIdSeen 0.0161 0.0082 0.0141 0.0131 19.8364smsRepliedEventsLatencyMedian 0.0093 0.0085 0.0096 0.0093 18.7719bluetoothIdsMoreThan04TimeSlotsSeen 0.0114 0.0066 0.0124 0.0110 15.6738bluetoothMaxTimeForWhichIdSeen 0.0141 0.0072 0.0095 0.0097 15.2546bluetoothTotalEntropyMillerMadow 0.0058 0.0017 0.0035 0.0035 13.9000bluetoothTotalEntropyShannon 0.0065 0.0009 0.0060 0.0050 13.1826callMeanInterEventTimePerDay -0.0003 -0.0000 0.0014 0.0009 13.1180incomingAndOutgoingCallsPerDay -0.0000 -0.0002 0.0024 0.0015 12.3962bluetoothQ50TimeForWhichIdSeen 0.0104 0.0104 0.0091 0.0095 12.2270callStandardDeviationInterEventTimePerDay 0.0004 -0.0005 0.0007 0.0004 10.1723bluetoothIdsMoreThan19TimeSlotsSeen 0.0087 0.0033 0.0089 0.0077 9.7388incomingCallsPerDay 0.0003 -0.0005 0.0009 0.0005 9.6572outgoingContactsToInteractionsRatioPerDay 0.0005 -0.0004 0.0013 0.0009 9.2016callsInitiatedRatioPerDay -0.0001 -0.0001 0.0014 0.0008 9.0245entropyMillerMadowCallsOutgoingWindow3Days 0.0001 -0.0008 0.0014 0.0008 8.7199bluetoothIdsMoreThan09TimeSlotsSeen 0.0074 0.0046 0.0040 0.0046 8.6006bluetoothQ75TimeForWhichIdSeen 0.0027 0.0018 0.0032 0.0028 8.4454outgoingCallsPerDay -0.0001 -0.0001 0.0012 0.0007 8.336817 / 49
  18. 18. Top-20 Features CorrelationagreeablenessbluetoothIdsMoreThan04TimeSlotsSeenbluetoothMaxTimeForWhichIdSeenbluetoothQ90TimeForWhichIdSeenbluetoothQ95TimeForWhichIdSeenbluetoothTotalEntropyMillerMadowbluetoothTotalEntropyShannoncallMeanInterEventTimePerDayconscientiousnessextraversionhumidityincomingAndOutgoingCallsPerDaymeanTemperatureneuroticismopennesspressuresmsRepliedEventsLatencyMediantotalPrecipitationvisibilitywindSpeedagreeablenessbluetoothIdsMoreThan04TimeSlotsSeenbluetoothMaxTimeForWhichIdSeenbluetoothQ90TimeForWhichIdSeenbluetoothQ95TimeForWhichIdSeenbluetoothTotalEntropyMillerMadowbluetoothTotalEntropyShannoncallMeanInterEventTimePerDayconscientiousnessextraversionhumidityincomingAndOutgoingCallsPerDaymeanTemperatureneuroticismopennesspressuresmsRepliedEventsLatencyMediantotalPrecipitationvisibilitywindSpeedVar1Var20.00.51.0value18 / 49
  19. 19. ResultsFinal Classifier Performance Metrics ComparisonTraining set Test setAccuracy 0.8081 0.8036Kappa 0.5879 0.5743AccuracyLower 0.8004 0.7878AccuracyUpper 0.8156 0.8187AccuracyNull 0.6415 0.6419AccuracyPValue 2.139e-303 8.826e-73McnemarPValue 5.647e-208 1.738e-5719 / 49
  20. 20. ResultsFinal Classifier Confusion Matrix for Training Set-1 0 1-1 782 119 750 153 1170 1451 600 903 6448Final Classifier Confusion Matrix for Test Set-1 0 1-1 197 30 140 34 274 371 152 243 161620 / 49
  21. 21. ResultsWhat We Learnt: Final Model ROC curveSpecificitySensitivity0.00.20.40.60.81.01.0 0.8 0.6 0.4 0.2 0.0AUC: 0.84421 / 49
  22. 22. LimitationsDataData loss not registered as NA’sBatteryTemporal resolutionModelRequires personality dataRequires 1 week data collection periodNot tested on diverse cultural groups22 / 49
  23. 23. SummaryAutomatic recognition of people’s daily happiness frommobile phone data is feasible.Accuracy is approaching the results of multi-modalobtrusive methods.Future work should be focused on multi-step recognitionmodel development.23 / 49
  24. 24. Thank you!{andrey.bogomolov@unitn.it}24 / 49
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