Northwestern University

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HABits Lab

To Change Behavior We Must Understand, Sense, Detect, Predict and Prevent

Understand

Human Computer Interaction
Focus Groups/Interviews and Surveys

Example: “Positive correlation between caloric intake and eating behaviors”

Sense

mHealth Sensor Systems
Passive Sensing

Example: “Build mHealth Sensor systems to detect overeating and its contextual factors.”

Detect

Segmentation
Feature Extraction
Low Level Machine Learning

Example: “Applying bag-of-words to segment and detect feeding gestures”

Predict

High Level Machine Learning
Statistical Analysis
Behavior Models

Example: “Applying unsupervised clustering algorithms to determine behavioral phenotypes to overeating”

Prevent

Behaviorist
Interventionist
Medical Expert

Example: “Collaborate with domain experts to test interventions”

Recent Class Projects

Interactions: Doctor Patient Graph

A simple use case of our app is for detecting individuals at risk of possible infection spread.

VIBES

Mental health is an area of concern in the United States. It is estimated that 1 of 5 adults suffer from at least one mental illness.

Body Image Myrror

Introducing Myrror, the device that seeks to revolutionize the prevention of eating disorders and overexercising.

About The Lab

We design, build and analyze end-to-end mHealth systems, while focusing on processing its data to help answer health-related questions.

We focus on signal processing and machine learning techniques to process time-series data generated from passive sensors. To advance existing sensing techniques, a part of our lab focuses on the design of embedded systems.

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.

About The Lab

Meet Team

Accepting Students

HABits Lab is seeking students with strong interest and competency in some combination of the following areas: mobile health, big data, embedded systems, machine learning, human-computer interaction, and wireless sensors.

Join Lab

Research

Our research aims to understand and improve people’s habits through data analytics and sensing in hopes of improving quality of life, preventing hospitalization and reducing healthcare costs.

Explore

Why HABits Lab?

What We Do

We design, build and analyze end-to-end mHealth systems, while focusing on processing its data to help answer health-related questions. We focus on signal processing and machine learning techniques to process time-series data generated from passive sensors. To advance existing sensing techniques, a part of our lab focuses on the design of embedded systems.

What We Envision

We envision a world where health costs and health care is manageable both in the U.S. and worldwide, where the majority of society can live a fulfilling life, and where individuals can achieve their full potential. We envision the advancement of technology to be used to help people regain control of their health problems and behaviors, leading them towards a balanced healthy lifestyle they can maintain.

Who We Are

We are researchers bridging the fields of computer science and preventive medicine, empowering people with new technology and making the dream of precision medicine a reality.

What They Say?

Nabil Alshurafa Ph.D., Director

Humans are creatures of habit, a routine pattern of unconscious behavior. Some habits destroy, others improve one’s quality of life. Recognizing patterns of behavior given massive amounts of data is an area that computers surpass humans. I want to use the power of machine learning and human computer interaction to help people understand their health habits and ultimately improve the quality of their life. This is why I joined HABits Lab.

Rawan Alharbi Ph.D., Student

If the greater the challenges your met with, the more excited you feel, the more you know you found the right research lab.

Shibo Zhang Ph.D., Student