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WH2014 Session: Machine learning-based analysis of smartphone usage pattern and

Date post: 19-Jun-2015
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Wireless Health 2014 Conference Demo and Abstract Session 1 featuring speaker Galen Chin-Lun Hung.
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WLSA CONVERGENCE SUMMIT MACHINE LEARNING-BASED ANALYSIS OF SMARTPHONE USAGE PATTERN AND ITS ASSOCIATION WITH NEGATIVE HUMAN EMOTION GALEN CHIN-LUN HUNG
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Page 1: WH2014 Session: Machine learning-based analysis of smartphone usage pattern and

WLSACONVERGENCE SUMMIT

MACHINE LEARNING-BASED ANALYSIS OF SMARTPHONE USAGE PATTERN AND ITS ASSOCIATION WITH NEGATIVE HUMAN EMOTION

GALEN CHIN-LUN HUNG

Page 2: WH2014 Session: Machine learning-based analysis of smartphone usage pattern and

What you do on the smartphone predicts how

you feel.

Page 3: WH2014 Session: Machine learning-based analysis of smartphone usage pattern and

Our model predicts 3 negative emotions based on the smartphone

usage pattern in the past two hours, with an average accuracy rate of

83.2%

Pleasure AnxietyStress

No feature selectionSelected model

Page 4: WH2014 Session: Machine learning-based analysis of smartphone usage pattern and

DEMO & ABSTRACT PRESENTATION SESSION #11.4 RESEARCH ABSTRACT

MACHINE LEARNING-BASED ANALYSIS OF SMARTPHONE USAGE PATTERN AND ITS ASSOCIATION WITH NEGATIVE HUMAN

EMOTIONGALEN CHIN-LUN HUNG; PEI-CHING YANG; CHIA-CHI CHANG;

JUNG-HSIEN CHIANG

This model has a potential for real-time treatment by envisioning negative emotions

and providing preventive interventions before the users actually 'feel bad’

Page 5: WH2014 Session: Machine learning-based analysis of smartphone usage pattern and

WLSACONVERGENCE SUMMIT

www.wirelesshealth2014.org


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