Conference Paper2014Cited by 55

Are we there yet?

Mahbubur Rahman, Rummana Bari, Amin Ahsan Ali, Moushumi Sharmin, Andrew Raij, Karen Hovsepian, Syed Monowar Hossain, Emre Ertin, Ashley Kennedy, David H Epstein, others, Michelle Jobes, J. Gayle Beck, Satish Kedia, Kenneth D. Ward, Mustafa al'Absi, Santosh Kumar

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

ACM, pp. 479-488

  • Good Health and Well-being — Relevance: 88%

Abstract

Stress can lead to headaches and fatigue, precipitate addictive behaviors (e.g., smoking, alcohol and drug use), and lead to cardiovascular diseases and cancer. Continuous assessment of stress from sensors can be used for timely delivery of a variety of interventions to reduce or avoid stress. We investigate the feasibility of continuous stress measurement via two field studies using wireless physiological sensors --- a four-week study with illicit drug users (n = 40), and a one-week study with daily smokers and social drinkers (n = 30). We find that 11+ hours/day of usable data can be obtained in a 4-week study. Significant learning effect is observed after the first week and data yield is seen to be increasing over time even in the fourth week. We propose a framework to analyze sensor data yield and find that losses in wireless channel is negligible; the main hurdle in further improving data yield is the attachment constraint. We show the feasibility of measuring stress minutes preceding events of interest and observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.

Keywords

  • social drinkers
  • daily smokers
  • illicit drug users
  • self-reported stress
  • learning effects

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

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