R
Rummana Bari
Researcher at University of Memphis
Publications - 9
Citations - 292
Rummana Bari is an academic researcher from University of Memphis. The author has contributed to research in topics: Internal medicine & Electron. The author has an hindex of 5, co-authored 8 publications receiving 235 citations. Previous affiliations of Rummana Bari include Bangladesh University of Engineering and Technology.
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Proceedings ArticleDOI
Assessing the availability of users to engage in just-in-time intervention in the natural environment
Hillol Sarker,Moushumi Sharmin,Amin Ahsan Ali,Md. Mahbubur Rahman,Rummana Bari,Syed Monowar Hossain,Santosh Kumar +6 more
TL;DR: A first step in modeling users' availability is taken by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study and finding that users are least available at work and during driving, and most available when walking outside.
Proceedings ArticleDOI
Are we there yet?: feasibility of continuous stress assessment via wireless physiological sensors
Md. Mahbubur Rahman,Rummana Bari,Amin Ahsan Ali,Moushumi Sharmin,Andrew Raij,Karen Hovsepian,Syed Monowar Hossain,Emre Ertin,Ashley P. Kennedy,David H. Epstein,Kenzie L. Preston,Michelle L. Jobes,J. Gayle Beck,Satish Kedia,Kenneth D. Ward,Mustafa al'Absi,Santosh Kumar +16 more
TL;DR: The feasibility of measuring stress minutes preceding events of interest is shown and the sensor-derived stress to be rising prior to self-reported stress and smoking events are observed and a framework to analyze sensor data yield is proposed and found that losses in wireless channel is negligible.
Proceedings ArticleDOI
mSieve: differential behavioral privacy in time series of mobile sensor data
Nazir Saleheen,Supriyo Chakraborty,Nasir Ali,Mahbubur Rahman,Syed Monowar Hossain,Rummana Bari,Eugene H. Buder,Mani Srivastava,Santosh Kumar +8 more
TL;DR: This work defines a new behavioral privacy metric based on differential privacy and proposes a novel data substitution mechanism to protect behavioral privacy and demonstrates that it is possible to retain meaningful utility, in terms of inference accuracy, while simultaneously preserving the privacy of sensitive behaviors.
Journal ArticleDOI
Continuous in-the-field measurement of heart rate: Correlates of drug use, craving, stress, and mood in polydrug users
Ashley P. Kennedy,David H. Epstein,Michelle L. Jobes,Daniel Agage,Matthew Tyburski,Karran A. Phillips,Amin Ahsan Ali,Rummana Bari,Syed Monowar Hossain,Karen Hovsepian,Md. Mahbubur Rahman,Emre Ertin,Santosh Kumar,Kenzie L. Preston +13 more
TL;DR: High-yield, high-quality heart-rate data can be obtained from drug users in their natural environment as they go about their daily lives, and the resultant data robustly reflect episodes of cocaine and heroin use and other mental and behavioral events of interest.
Journal ArticleDOI
rConverse: Moment by Moment Conversation Detection Using a Mobile Respiration Sensor
Rummana Bari,Roy Adams,Md. Mahbubur Rahman,Megan Battles Parsons,Eugene H. Buder,Santosh Kumar +5 more
TL;DR: A Conditional Random Field, Context-Free Grammar (CRF-CFG) based conversation model is presented, called rConverse, to classify respiration cycles into speech or non-speech, and subsequently infer conversation episodes, and achieves 82.7% accuracy for speech/non-speech classification.