Analysing Daily Behaviours with Large-Scale Smartphone Data

Analysing Daily Behaviours with Large-ScaleSmartphone Data

@neal_lathiaComputer LaboratoryUniversity of Cambridge

BackgroundSmartphones as Research ToolsCase 1: Public TransportCase 2: Subjective Wellbeing & BehaviourCase 3: Behavioural InterventionChallenges, Opportunities, Questions

Background

Smartphones as Research Tools

“by 2025, when most of today’s psychology undergraduates will be in their mid-30s, morethan 5 billion people on our planet will be usingultra-broadband, sensor-rich smartphones farbeyond the abilities of today’s iPhones, Androids,and Blackberries.”

Miller

AccelerometerGPS / Wi-FiGyroscopeBluetoothMicrophoneHumidityTemperaturePhone / Text LogsDevice LogsSocial Media APIsApp Usage

Accelerometer | Physical ActivityGPS / Wi-Fi | MobilityGyroscope | OrientationBluetooth | Co-LocationMicrophone | Ambient AudioHumidity | EnvironmentTemperature | EnvironmentPhone / Text Logs | SocialisingDevice Logs | NetworkSocial Media APIs | SocialisingApp Usage | /Information Needs

Case 1: Public Transport

N. Lathia, L. Capra. Tube Star: Crowd-Sourced Experiences on PublicTransport. In 11th International Conference on Mobile and UbiquitousSystems. London, December 2014.

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.

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)

Method1. Collect Wi-Fi scans that match“Virgin Media Wi-Fi”

2. Manually label “unknown”stations (“Where are you?”)

3. Apply heuristic-based clusteringalgorithm to determine stationvisits, paths, travel times.

Preliminary Data34 users; 234,769 Wi-Fi scans,106,793

Sequences of Wi-Fi Connections

Oyster Card transaction

Journey Planner:Victoria Line to Oxford CircusCentral Line to Mile End27 Minutes

Measured:Victoria Line to VictoriaCircle/District to Mile End29 minutes

21:54:02

21:56:06

21:58:13

22:12:00

22:15:41

22:23:05

Oyster card transaction

Journey Planner:Piccadilly Line to HolbornCentral Line to West Ruislip56 Minutes

Measured:Piccadilly Line to King's CrossVictoria Line to Oxford CircusCentral Line to West Ruislip74 minutes

10:56:06

11:15:39

11:32:55

12:10:09

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

40%

10%

10%

5%

5%

41.70%64.7%

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?

Case 2: Subjective Wellbeing & Behaviour

N. Lathia, K. Rachuri, C. Mascolo, P. Rentfrow. Contextual Dissonance:Design Bias in Sensor-Based Experience Sampling Methods. In ACMInternational Joint Conference on Pervasive and Ubiquitous Computing.Zurich, Switzerland. September 2013.

N. Lathia, G. Sandstrom, P. Rentfrow, C. Mascolo. Happy People LiveActive Lives. In prep.

“A sample of 222 undergraduates was screenedfor high happiness using multiple confirmingassessment filters. We compared the upper 10%of consistently very happy people with averageand very unhappy people. The very happy peoplewere highly social, and had stronger romantic andother social relationships than less happygroups...”

Diener, Seligman. Very Happy People. In Psychological Science 13 (1). Jan 2002.

angry anxious lonely

relaxedenthusiasticcalm

Hemminki, Nurmi, Tarkoma. Accelerometer-Based Transportation Mode Detection onSmartphones. 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

Example: 85 Users

Case 3: Behavioural Intervention

Challenges & Questions

1. Software Engineering / Expectations2. Marketing3. Control over target population4. Understanding sensor data5. Writing code6. Finding research value

1. Blurred lines between research and practice2. High potential for multi-disciplinary impact3. Cheap to roll-out to huge audiences4. Accessible to 'everyone'5. Wearables are coming!

Can I run a study likeEmotion Sense?

Yes, with Easy M. Ageneralised sensor-enhanced experiencesampling tool.

@neal_lathia

neal.lathia@cl.cam.ac.uk http://www.cl.cam.ac.uk/~nkl25/