we design, build and analyze end-to-end mhealth
systems, while focusing on processing its data to help
answer health-related questions.
Ph.D., Director of HABits Lab
Our lab is at the intersection of computer science and preventive medicine. Through analysis of continuous streams of data provided by smartphones and wearable sensors, we use signal processing intelligence and machine learning to understand a person’s moment-to-moment behavior, psychological states, and environmental context in which the behavior occurs. We design, build, and analyze end-to-end mobile health (mHealth) systems, while focusing on processing its data to help answer health-related questions.
It is the humanity within us and the desire to improve quality of life and healthcare costs that guide our solutions to the persisting health problems of our time through computer science and behavior science based research in passive sensing data analytics. Our goal is to advance our ability to understand, detect, predict, and ultimately prevent problematic health habits. We are the Health Aware Bits (HABits) Lab!
• Human Computer Interaction
• Focus Groups/Interviews and Surveys
• mHealth Sensor Systems
• Passive Sensing
• Feature Extraction
• Low Level Machine Learning
• High Level Machine Learning
• Statistical Analysis
• Behavior Models
• Medical Expert
EAT: A Reliable Eating Assessment Technology for Free-living Individuals.
WildCam: A Privacy Conscious Wearable Eating Detection Camera People will Actually Wear in the Wild
VibroScale: Turning your smartphone into a weighing scale
BehaviorSight: Privacy enhancing wearable system to detect health risk behaviors in real-time
NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions
To Mask or Not to Mask? Balancing Privacy with Visual Confirmation Utility in Activity-Oriented Wearable Cameras
Micro-Stress EMA: A Passive Sensing Framework for Detecting in-the-wild Stress in Pregnant Mothers
EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models
Undergraduates, graduates, and Postdocs trained in mHealth.
In NIH, NSF, and Foundation funding.
People enrolled and participated in our studies to advance wearable technology and understand human behavior.
$3,868,150 / PI: Nabil Alshurafa / 2021 - 2026
$233,587 / PI: Nabil Alshurafa / 2020 - 2022
$606,713 / PI: Nabil Alshurafa / 2020 - 2023
$315,000 / PI: Nabil Alshurafa / 2019 - 2022
$850,000 / PI: Nabil Alshurafa / 2018 - 2023
Foodtrk: Track meals and snacks with pictures of food and questionnaire for research
WristSense 2021: Workshop on Sensing Systems and Applications Using Wrist Worn Smart Devices
We’re are a friendly , forward thinking collective, an approachable team with a can-do attitude. Our curiosity and breadth of experience means we can turn our minds to new challenges, combining the need for functionality with a desire for aesthetic value.
Director, Institute for Public Health and Medicine (IPHAM) - Center for Behavior and Health. Professor in Preventive Medicine-Behavioral Medicine, Psychiatry and Behavioral Sciences and Weinberg College of Art
Professor of Electrical and Computer Engineering, Northwestern University, Joseph Cummings Professor, McCormick School of Engineering
Assistant Professor of Preventive Medicine (Behavioral Medicine), Clinical Health Psychologist
Research Professor of Dermatology
Assistant Professor of Electrical and Computer Engineering, Assistant Professor of Computer Science, Northwestern University, Director of Ka MoaMoa Lab
Professor of Medical Social Sciences, Northwestern University, Social Psychologist
Vice Chair for Scientific & Faculty Development, Department of Medical Social Sciences Director, Institute for Innovations in Developmental Sciences Professor of Medical Social Sciences,
It is the humanity within us and the desire to improve quality of life and healthcare costs that guide our solutions to the persisting health problems of our time through computer science and behavior science based research in passive sensing data analytics; helping us advance our ability to understand, detect, predict, and ultimately prevent problematic health habits. We are the Health Aware Bits (HABits) Lab.