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Analysing Daily Behaviours with Large-Scale Smartphone Data

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Invited talk @ UCL CASA, 11/03/2015

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Analysing Daily Behaviours with Large-Scale Smartphone Data

  1. 1. Analysing Daily Behaviours with Large-Scale Smartphone Data @neal_lathia Computer Laboratory University of Cambridge
  2. 2. Background Smartphones as Research Tools Case 1: Public Transport Case 2: Subjective Wellbeing & Behaviour Case 3: Behavioural Intervention Challenges, Opportunities, Questions
  3. 3. Background
  4. 4. Smartphones as Research Tools
  5. 5. “by 2025, when most of today’s psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today’s iPhones, Androids, and Blackberries.” Miller
  6. 6. Accelerometer GPS / Wi-Fi Gyroscope Bluetooth Microphone Humidity Temperature Phone / Text Logs Device Logs Social Media APIs App Usage
  7. 7. Accelerometer | Physical Activity GPS / Wi-Fi | Mobility Gyroscope | Orientation Bluetooth | Co-Location Microphone | Ambient Audio Humidity | Environment Temperature | Environment Phone / Text Logs | Socialising Device Logs | Network Social Media APIs | Socialising App Usage | /Information Needs
  8. 8. Case 1: Public Transport N. Lathia, L. Capra. Tube Star: Crowd-Sourced Experiences on Public Transport. In 11th International Conference on Mobile and Ubiquitous Systems. London, December 2014.
  9. 9. Conclusion: potential for smartphones as a near- real time passenger surveying tool to collect qualitative trip data. Major Limitation: amount of data received. Still relies on reported behaviour.
  10. 10. Accelerometer (activity) GPS / Wi-Fi (location) Gyroscope (orientation) Bluetooth (co-location) Microphone (audio context) Environment (temperature) Phone / Text Logs (sociability) Device Logs (e.g., network) Social Media APIs (crowdsourcing) App Usage (information seeking)
  11. 11. Method 1. Collect Wi-Fi scans that match “Virgin Media Wi-Fi” 2. Manually label “unknown” stations (“Where are you?”) 3. Apply heuristic-based clustering algorithm to determine station visits, paths, travel times. Preliminary Data 34 users; 234,769 Wi-Fi scans, 106,793
  12. 12. Sequences of Wi-Fi Connections
  13. 13. Oyster Card transaction
  14. 14. Journey Planner: Victoria Line to Oxford Circus Central Line to Mile End 27 Minutes
  15. 15. Measured: Victoria Line to Victoria Circle/District to Mile End 29 minutes 21:54:02 21:56:06 21:58:13 22:12:00 22:15:41 22:23:05
  16. 16. Oyster card transaction
  17. 17. Journey Planner: Piccadilly Line to Holborn Central Line to West Ruislip 56 Minutes
  18. 18. Measured: Piccadilly Line to King's Cross Victoria Line to Oxford Circus Central Line to West Ruislip 74 minutes 10:56:06 11:15:39 11:32:55 12:10:09
  19. 19. Capturing Routes: Given an O-D pair, count the % of times that another station appears as an intermediary Observing Mistakes? E.g., 2.18% of trips from Pimlico to Victoria Station go via Green Park (wrong direction). Non-Adjacent Pairs of Wi-Fi Connections
  20. 20. 40% 10% 10% 5%
  21. 21. 5% 41.70% 64.7%
  22. 22. Continued (lessened) limitations: Data is not “complete” - phones do not always connect. Data is now “noisy” by capturing route errors, “strange” behaviours. Direct application: Transport route choices in individuals With more scale: Granular origin-destination + distributions of route data. With more data: I.e., precise locations of Wi-Fi hotspots (e.g., platform, entrance) With more sensors: What actual behaviours are occurring?
  23. 23. Case 2: Subjective Wellbeing & Behaviour N. Lathia, K. Rachuri, C. Mascolo, P. Rentfrow. Contextual Dissonance: Design Bias in Sensor-Based Experience Sampling Methods. In ACM International Joint Conference on Pervasive and Ubiquitous Computing. Zurich, Switzerland. September 2013. N. Lathia, G. Sandstrom, P. Rentfrow, C. Mascolo. Happy People Live Active Lives. In prep.
  24. 24. “A sample of 222 undergraduates was screened for high happiness using multiple confirming assessment filters. We compared the upper 10% of consistently very happy people with average and very unhappy people. The very happy people were highly social, and had stronger romantic and other social relationships than less happy groups...” Diener, Seligman. Very Happy People. In Psychological Science 13 (1). Jan 2002.
  25. 25. angry anxious lonely relaxedenthusiasticcalm
  26. 26. Hemminki, Nurmi, Tarkoma. Accelerometer-Based Transportation Mode Detection on Smartphones. In ACM Sensys 2013. Statistical: mean, standard deviation, median, etc. Time: auto-correlation, mean-crossing rate, etc. Frequency: FFT, spectral energy, etc. Peak: volume, intensity, skewness, etc. Segment: e.g., velocity change rate
  27. 27. Example: 85 Users
  28. 28. Case 3: Behavioural Intervention
  29. 29. Challenges & Questions
  30. 30. 1. Software Engineering / Expectations 2. Marketing 3. Control over target population 4. Understanding sensor data 5. Writing code 6. Finding research value
  31. 31. 1. Blurred lines between research and practice 2. High potential for multi-disciplinary impact 3. Cheap to roll-out to huge audiences 4. Accessible to 'everyone' 5. Wearables are coming!
  32. 32. Can I run a study like Emotion Sense? Yes, with Easy M. A generalised sensor- enhanced experience sampling tool.
  33. 33. @neal_lathia neal.lathia@cl.cam.ac.uk http://www.cl.cam.ac.uk/~nkl25/

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