MoodScope: Asia! Sensing mood from smartphone usage pa5erns. Yunxin Liu Nicholas D. Lane. Robert Likamwa Lin Zhong

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1 MoodScope: Sensing mood from smartphone usage pa5erns Robert Likamwa Lin Zhong Yunxin Liu Nicholas D. Lane Asia!

2 Mood-Enhanced Apps! Personal" analytics! Social" ecosystems! Media " recommendation! 2 of 30!

3 Affective Computing (Mood and Emotion) Biometric- based (Skin conductivity, Temperature, Pulse rate) Highly temporal High cost of deployment Hassle Audio/Video- based (AffectAura, EmotionSense) Captures expressions Power hungry Slightly invasive 3 of 30

4 Can your mobile phone infer your mood? From already-available, low-power information?* * No audio/video sensing, no body- instrumentation

5 MoodScope Affective Computing Usage Trace- based (MoodScope) Passive, Continuous How to model mood? Biometric- based Very direct, Fine- grained High cost of deployment Audio/Video- based Captures expressions Power hungry Slightly invasive 5 of 30

6 Mood is a persistent long-lasting state o Lasts hours or days o Emotion lasts seconds or minutes a strong social signal o Drives communications o Drives interactions o Drives activity patterns 6 of 30

7 sad depressed stressed nervous bored calm a5entive excited relaxed happy Circumplex model (Russell 1980) 7 of 30

8 How is the user communicating?

9 What apps is the user using?

10 f ( usage ) = mood 10 of 30

iphone Livelab Logger Web history Phone call history

Runs as shell Hash private data Nightly uploads Adapted

Kortum, "ʺLivelab: Measuring Wireless Networks And

11 iphone Livelab Logger Web history Phone call history Sms history history Location history App usage Runs as shell Hash private data Nightly uploads Adapted From C. Shepard, A. Rahmati, C. Tossel, L. Zhong, And P. Kortum, "ʺLivelab: Measuring Wireless Networks And Smartphone Users In The Field,"ʺ In Hotmetrics, of 30

12 Mood Journaling App User- base 32 users aged between 18 and females 12 of 30

13 Inference Detect a mood pattern Validate with only 60 days of data Wide range of candidate usage data Low computational resources 13 of 30

14 Daily Mood Averages Separate pleasure, activeness dimension Take the average over a day Σ( ) 4 14 of 30

15 Exploring Features Communication o SMS o o Phone Calls Consider Top 10 Histograms? How many phone calls were made to #1? #2? #10? To whom? o # messages o Length/Duration? How much time was spent on calls to #1? #2? #10? 15 of 30

16 Exploring Features Communication o SMS o o Phone Calls Usage Activity o Applications o Websites visited o Location History To whom? o # messages o Length/Duration Which (app/site/location)? o # instances 16 of 30

17 Previous Mood Use previous 2 pairs of mood labels 17 of 30

18 Data Type Histogram by: Dimensions contacts # Messages 10 # Characters 10 SMS contacts # Messages 10 # Characters 10 Phone call contacts # Calls 10 Call Duration 10 Website domains # Visits 10 Location Clusters # Visits 10 Apps # App launches 10 App Duration 10 Categories of Apps Previous Pleasure and Activeness Averages # App launches 12 App Duration 12 N/A 4 18 of 30

19 ?? Model Design Multi-Linear Regression o Minimize Mean Squared Error Leave-One-Out Cross-Validation Sequential Forward Feature Selection during training 19 of 30

20 Sequential Feature Selection Mean Squared Error Improvement of model as SFS adds more features (Each line is a different user) Number of Features Used 20 of 30

21 Sample Prediction Daily Mood Average Mood (Pleasure) Estimated Mood Days 21 of 30

22 Error distributions Error 2 of > 0.25 will misclassify a mood label 93% < 0.25 error Squared Error %ile 75%ile Users 22 of 30

23 vs. Strawman Models Models using full-knowledge of a user s data with LOOCV Model A: Assume User s Average Mood 73% Accuracy Model B: Assume User s Previous Mood 61% Accuracy MoodScope Training: 93% Accuracy. 23 of 30

24 Personalized Training 100% Model Accuracy 80% 60% 40% 20% Incremental personalized model All- user model accuracy 0% Training Days 24 of 30

25 Personalized/All- user Hybrid Training 100% 80% Model Accuracy 60% 40% 20% 0% Incremental personalized model Hybrid mood model Training Days 25 of 30

26 Resource- friendly Implementation Phone Cloud Inferred Mood Mood Model Mood Model Current Usage Model Training Mood Inputs/ Usage Logs Mood and Usage History 26 of 30

27 Inferred Mood API

28 MoodScope: Sensing mood from smartphone usage pa5erns Robustly (93%) detect each dimension of daily mood o On personalized models o Starts out with 66% on generalized models Validate with 32 users x 2 months worth of data Simple resource-friendly implementation

Context-for-Wireless: Context-Sensitive Energy- Efficient Wireless Data Transfer

Context-for-Wireless: Context-Sensitive Energy- Efficient Wireless Data Transfer Ahmad Rahmati and Lin Zhong Rice Efficient Computing Group (recg.org) Dept. of Electrical & Computer Engineering Rice University