Recent News Boyang's work, 'Resource-Constrained Eating Detection: Enabling Just-in-Time Interventions via Wrist-Worn Sensors' was awarded Live Research Spotlight at SBM 2025.
Farzad, Boyang, Jiayi, Chris, and Soroush will present works at the 2025 Meeting of the Society of Behavioral Medicine (SBM).
BSN24 organized by Conference Co-Chair Nabil & OC Co-Chairs Chris, Bonnie, Glenn, and Mahdi.
A machine-learned model for predicting weight loss success using weight change features early in treatment
Glenn will spend time in California as part of his internship with Dolby
Glenn's work, 'HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications' now appears in ACM IMWUT.
Awarded Paper: Deep learning in human activity recognition with wearable sensors: A review on advances
Both awards were given to Farzad's work, 'Defining Overeating Phenotypes in Naturalistic Settings: Leveraging Mobile Health and Machine Learning'.
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.
$331,781 / PI: Nabil Alshurafa / 2024 - 2027
$3,868,150 / PI: Nabil Alshurafa / 2021 - 2026
$233,587 / PI: Nabil Alshurafa / 2020 - 2022
$606,713 / PI: Nabil Alshurafa / 2020 - 2023
$299,471 / PI: Nabil Alshurafa / 2019 - 2022
Developing and comparing a new BMI inclusive energy expenditure algorithm on wrist-worn wearables
The Role of Eating Time in the Associations Between Hunger, Appetite, Thirst, and Energy Intake"
A machine-learned model for predicting weight loss success using weight change features early in treatment
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We design, build, develop, and analyze. We are HABits Lab. HABits Lab is Affiliated With Northwestern Feinberg School of Medcine and Northwestern McCormick School of Engineering.
