About

Nabil Alshurafa is an Associate Professor of Preventive Medicine at Northwestern University, with courtesy appointments in Computer Science and Electrical and Computer Engineering. He holds a PhD in Computer Science and Wireless Health from UCLA, as well as a master's and bachelor's degree in Computer Science from UCLA. His research interests encompass health-related topics, including body sensor networks, behavioral science, data analytics, embedded systems, algorithm design, nutrition monitoring, activity recognition, obesity, and cardiovascular disease. His work focuses on leveraging technology and data to improve health outcomes and understand health behaviors through innovative sensor and data analysis techniques.

Research topics

• Computer Science
• Data science
• Psychology
• Human–computer interaction
• Embedded system
• Internet privacy
• Artificial Intelligence
• World Wide Web
• Computer Security
• Mathematics
• Social psychology
• Engineering
• Medicine
• Applied psychology

Selected publications

• Mixed-effects location scale modeling of stress and contextual factors on overeating: a real-world observational study

  International Journal of Obesity · 2026-01-20

  articleOpen accessSenior authorCorresponding

  OBJECTIVE: The objective of our 14-day technology-supported free-living study was to assess how psychological, environmental, and social factors affect overeating among participants with obesity. METHODS: ), who collectively logged 2004 meals, wore and used study devices for meal verification, and completed daily food recalls administered by dietitians. Participants reported on stress, affect, hunger, and meal contexts through Ecological Momentary Assessments (EMA). To explore the factors influencing caloric intake per meal, we employed a two-level mixed-effects location scale model, capturing both between-subject (BS) and within-subject (WS) factors based on the EMA data. This is a secondary analysis of the SenseWhy study, focusing on the association between stress and intake. RESULTS: Our analysis identified six BS factors (e.g., stress, perception of overeating, restaurant food, later meals, pleasure-seeking meal) and ten WS factors (e.g., biological hunger, perceived overeating, uncontrolled eating, social eating, restaurant food, snacks) to be significantly associated with caloric intake. Notably, participants who were more stressed, on average, consumed more calories (0.74; p = 0.002) with high consistency (-0.7; p = 0.048) between individuals. When stressed and not at home, participants consumed less calories (-0.62; p = 0.0043). CONCLUSION: Conventional strategies for managing stress-related overeating fall short. Effectively addressing overeating requires an understanding of both psychological and contextual factors.

  PublisherOA PDFDOI

• Unveiling overeating patterns within digital longitudinal data on eating behaviors and contexts

  npj Digital Medicine · 2025-09-17

  articleOpen accessSenior author

  Overeating contributes to obesity and poses a significant public health threat. The SenseWhy study (2018-2022) monitored 65 individuals with obesity in free-living settings, collecting 2302 meal-level observations (48 per participant), using an activity-oriented wearable camera, a mobile app, and dietitian-administered 24-hour dietary recalls. Micromovements (e.g., bites, chews) were manually labeled from 6343 hours of footage spanning 657 days. Psychological and contextual information was gathered before and after meals through Ecological Momentary Assessments (EMAs). We predicted overeating episodes based on EMA-derived features and passive sensing data (mean AUROC = 0.86; mean AUPRC = 0.84). Using semi-supervised learning on EMA-derived features alone, we identified five distinct overeating phenotypes: "Take-out Feasting," "Evening Restaurant Reveling," "Evening Craving," "Uncontrolled Pleasure Eating," and "Stress-driven Evening Nibbling." These results highlight the complex interplay between behavioral, psychological, and contextual factors associated with overeating, providing a foundation for personalized interventions.

  PublisherOA PDFDOI

• Digital Phenotyping via Passive Network Traffic Monitoring: Feasibility and Acceptability in University Students (Preprint)

  2025-09-24

  articleOpen access

  <sec> <title>BACKGROUND</title> Digital behaviors such as sleep, social interaction, and productivity reflect how individuals structure daily life. Among university students, online activity patterns mirror academic schedules, social rhythms, and lifestyle habits, with disruptions linked to sleep, stress, and well-being. Existing approaches—including wearables, apps, and surveys—yield useful insights but depend on self-report or active participation, limiting adherence in real-world use. Passive sensing of network traffic provides a scalable and less burdensome alternative, enabling unobtrusive capture of smartphone usage patterns while preserving privacy. </sec> <sec> <title>OBJECTIVE</title> This study evaluated whether encrypted smartphone network traffic, collected via a standard virtual private network (VPN), can be used to capture patterns of digital behavior. We assessed feasibility (sustained data capture) and acceptability (usability, burden, and privacy perceptions), and examined whether traffic-derived features reveal aspects of digital behavior—including timing, intensity, and regularity—relevant to health and daily functioning. </sec> <sec> <title>METHODS</title> We conducted a two-week prospective observational study at New York University. Thirty-eight students enrolled; 29 provided valid network data, 27 remained active for more than five days, and 25 completed the exit interview. Participants installed the WireGuard VPN client on personal smartphones, which enabled passive capture of encrypted network traffic. Feasibility was assessed across two domains: user retention and data coverage. Acceptability was evaluated using the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and semi-structured exit interviews. Beyond evaluating feasibility and acceptability, we conducted exploratory analyses that visualized traffic-derived features in relation to digital activity patterns. </sec> <sec> <title>RESULTS</title> Of the 29 participants who contributed valid data, 27 (93%) remained active for more than five days. Mean data coverage was 74.1% (median 77.1%). Participants contributed an average of 311.6 hours of monitored traffic (~13 days, SD 3.5), with totals ranging from 121 to 496 hours. Usability ratings were high (mean SUS score = 78) and perceived workload low (NASA-TLX scores minimal). Participants described the system as easy to install, unobtrusive, and generally trustworthy, though some reported temporarily disabling the VPN during activities they considered private. No inferential statistical tests were conducted; analyses were descriptive. Exploratory analyses indicated that traffic-derived features reflected daily digital activity rhythms and revealed distinctive lifestyle patterns, including gaming and irregular late-night food delivery use. </sec> <sec> <title>CONCLUSIONS</title> VPN-based monitoring of encrypted smartphone traffic was feasible and acceptable, enabling sustained passive data collection with minimal burden. The findings demonstrate the potential of this approach as a scalable and device-agnostic method for digital phenotyping—capable of capturing fine-grained behavioral rhythms while preserving privacy. With broader validation and deployment, the technique could expand the toolkit for studying health, well-being, and cognitive function in everyday life. </sec> <sec> <title>CLINICALTRIAL</title> Not applicable. This study was not registered as a clinical trial because it did not involve randomization. </sec>

  PublisherDOI

• The Role of Eating Time in the Associations Between Hunger, Appetite, Thirst, and Energy Intake

  Current Developments in Nutrition · 2025-05-01

  articleOpen accessSenior author

  Objectives: Food contamination is receiving heightened attention, but few comprehensive studies describe heavy metal content in foods or dietary exposure risks to heavy metals among vulnerable groups.We analyzed arsenic (As), cadmium (Cd), lead (Pb), and minerals in a market basket of foods from Montevideo, Uruguay and estimated daily dietary exposure in school children.Methods: From two 24-hr recalls for 862 low-average income children aged 7 we identified commonly consumed foods and beverages (~90% of the diet by frequency).We purchased fresh, frozen or ultra processed foods in several categories: milk, dairy & eggs (12 items), fruits (10), vegetables ( 14), breads & rolls (4), pastries & crackers (6), desserts & sweets (12), meats & cold cuts (19), fats & oils (2), cereals & legumes ( 9), frozen or instant foods (13), sauces & condiments (6).Multiple brands of the same food were pooled.Three samples were taken from each pooled item; digested with nitric and perchloric acid; analyzed via ICP-AES.Metal & mineral values were multiplied by reported food consumption to estimate dietary intake levels.Results: Preliminary findings suggest that As (< LOQ-1.6 g/ g), Cd (< LOQ-0.3 g/g), and Pb (< LOQ-355 g/g) in foods were generally low.Estimated dietary As, Cd, and Pb exposure ranged 0-194, 0.04-6.92,and 0.05-24.1 g/day, respectively.For lead >50% of children had dietary intake above current FDA guideline of 2.2 g/day.These diets provided 0.03-7.0,0.02-8.6,and 0.44-735.4mg/day of Fe, Zn and Ca, respectively.Conclusions: Food contamination with Pb and As is a potential concern in this population.Still, the same foods provide 3-4 orders of magnitude higher levels of minerals essential for growth and development compared to heavy metals.

  PublisherOA PDFDOI

• Multi-Modal Hand-to-Mouth Gesture Recognition in Activity-Oriented RGB-Thermal Footage (Student Abstract)

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 1 citations

  articleOpen accessSenior author

  Health-risk behaviors such as overeating and smoking have a profound impact on public health, making their monitoring and mitigation critical. Wearable RGB-Thermal cameras are being employed to monitor these behaviors by capturing hand-to-mouth (HTM) gestures, which are central to them. However, detection models relying on single modalities—either RGB or thermal—often struggle to accurately distinguish these confounding gestures due to inherent sensor limitations, such as sensitivity to lighting conditions or thermal occlusions. We present a family of fusion models that integrate RGB and thermal video data using early-, decision- , and a novel mid-fusion architecture, RGB-Thermal Fusion Video Network (RTFVNet), designed to enhance the recognition of HTM gestures associated with eating and smoking. Our evaluation shows that while decision fusion achieves the highest F1-score of 88% (0.44 TFLOPs), RTFVNet offers an optimal balance between performance (85%) and complexity (0.37 TFLOPs) for gesture classification of eating, smoking, and non-gesture activities.

  PublisherOA PDFDOI

• From reactive to proactive: Continuous protein monitoring for preventive health care

  Science · 2025-09-25 · 12 citations

  reviewOpen access

  Continuous biomarker monitoring is revolutionizing chronic disease management, with glucose monitoring for diabetes as the primary example. Given the success of this approach, a transition to continuous protein monitoring (CPM, a real-time, implantable or wearable technology) could similarly advance precision medicine. In this work, we review state-of-the-art CPM platforms and their prospective clinical impact across both chronic disorders-metabolic, cardiovascular, autoimmune, and neurodegenerative-and acute crises, such as sepsis and transplant dysfunction. We also highlight remaining barriers to widespread adoption, including sensor stability, robust machine learning models for live interpretation, and responsible data handling for patient privacy. With continued engineering and clinical validation, emerging biosensor technologies could transform disease management, facilitating earlier interventions and individualizing treatment strategies, ultimately improving patient outcomes.

  PublisherOA PDFDOI

• A Multimodal AI-Enabled Framework for Characterizing Overeating Behaviors and Consumption Patterns

  2025-11-03

  articleSenior author

  Overeating is a key contributor to obesity, yet identifying and characterizing its underlying causes remains challenging. While prior research has leveraged Ecological Momentary Assessment (EMA) to capture psychological and contextual factors in real-time, few studies have integrated EMA with passive sensing to uncover fine-grained, individualized consumption behaviors. In this work, we present a multimodal framework combining psychological and contextual data from a custom-built EMA app with validated camera-derived meal microstructure features from a neck-worn activity-oriented wearable camera. Across 41 participants, the camera captured 6,343 hours of footage over 312 days, yielding annotated bites, chews, meal start/end times, and dietitian-confirmed caloric intake. Using supervised contrastive learning, we generated meal-level representations, projected them using UMAP, and applied k-means clustering to identify behavioral phenotypes. We then conducted a z-score analysis to highlight features most distinctive to each cluster. Among the eight discovered groups, three consistently showed high purity for overeating meals (average purity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=0.99$</tex>), revealing nuanced, data-driven overeating phenotypes that may inform targeted intervention strategies.

  PublisherDOI

• THOR: Thermal-Guided Hand-Object Reasoning via Adaptive Vision Sampling

  Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2025-12-02

  articleOpen accessSenior author

  Wearable cameras are increasingly used as an observational and interventional tool for human behaviors by providing detailed visual data of hand-related activities. This data can be leveraged to facilitate memory recall for logging of behavior or timely interventions aimed at improving health. However, continuous processing of RGB images from these cameras consumes significant power impacting battery lifetime, generates a large volume of unnecessary video data for post-processing, raises privacy concerns, and requires substantial computational resources for real-time analysis. We introduce THOR, a real-time adaptive spatio-temporal RGB frame sampling method that leverages thermal sensing to capture hand-object patches and classify them in real time. We use low-resolution thermal camera data to identify moments when a person switches from one hand-related activity to another and adjust the RGB frame sampling rate by increasing it during activity transitions and reducing it during periods of sustained activity (when the system has enough information to identify the activity). Additionally, we use the thermal cues from the hand to localize the region of interest (i.e. , the hand-object interaction) in each RGB frame, allowing the system to crop and process only the necessary part of the image for activity recognition. We develop a wearable device to validate our method through an in-the-wild study with 14 participants and over 30 activities, and further evaluate it on Ego4D (923 participants across 9 countries, totaling 3,670 hours of video). Our results show that using only 3% of the original RGB video data, our method captures all the activity segments, and achieves a hand-related activity recognition F1-score (95%) comparable to using the entire RGB video (94%). Our work provides a more practical path for the longitudinal use of wearable cameras to monitor hand-related activities and health-risk behaviors in real time.

  PublisherDOI

• THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Sampling

  ArXiv.org · 2025-07-08

  preprintOpen accessSenior author

  Wearable cameras are increasingly used as an observational and interventional tool for human behaviors by providing detailed visual data of hand-related activities. This data can be leveraged to facilitate memory recall for logging of behavior or timely interventions aimed at improving health. However, continuous processing of RGB images from these cameras consumes significant power impacting battery lifetime, generates a large volume of unnecessary video data for post-processing, raises privacy concerns, and requires substantial computational resources for real-time analysis. We introduce THOR, a real-time adaptive spatio-temporal RGB frame sampling method that leverages thermal sensing to capture hand-object patches and classify them in real-time. We use low-resolution thermal camera data to identify moments when a person switches from one hand-related activity to another, and adjust the RGB frame sampling rate by increasing it during activity transitions and reducing it during periods of sustained activity. Additionally, we use the thermal cues from the hand to localize the region of interest (i.e., the hand-object interaction) in each RGB frame, allowing the system to crop and process only the necessary part of the image for activity recognition. We develop a wearable device to validate our method through an in-the-wild study with 14 participants and over 30 activities, and further evaluate it on Ego4D (923 participants across 9 countries, totaling 3,670 hours of video). Our results show that using only 3% of the original RGB video data, our method captures all the activity segments, and achieves hand-related activity recognition F1-score (95%) comparable to using the entire RGB video (94%). Our work provides a more practical path for the longitudinal use of wearable cameras to monitor hand-related activities and health-risk behaviors in real time.

  PublisherOA PDFDOI

• Co-designing prediction data visualizations for a digital binge eating intervention

  Translational Behavioral Medicine · 2025-01-01 · 1 citations

  articleOpen access

  BACKGROUND: Digital interventions can leverage user data to predict their health behavior, which can improve users' ability to make behavioral changes. Presenting predictions (e.g. how much a user might improve on an outcome) can be nuanced considering their uncertainty. Incorporating predictions raises design-related questions, such as how to present prediction data in a concise and actionable manner. PURPOSE: We conducted co-design sessions with end-users of a digital binge-eating intervention to learn how users would engage with prediction data and inform how to present these data visually. We additionally sought to understand how prediction intervals would help users understand uncertainty in these predictions and how users would perceive their actual progress relative to their prediction. METHODS: We conducted interviews with 22 adults with recurrent binge eating and obesity. We showed prototypes of hypothetical prediction displays for 5 evidence-based behavior change strategies, with the predicted success of each strategy for reducing binge eating in the week ahead (e.g. selecting to work on self-image this week might lead to 4 fewer binges while mood might lead to 1 fewer). We used thematic analysis to analyze data and generate themes. RESULTS: Users welcomed using prediction data, but wanted to maintain their autonomy and minimize negative feelings if they do not achieve their predictions. Although preferences varied, users generally preferred designs that were simple and helped them quickly compare prediction data across strategies. CONCLUSIONS: Predictions should be presented in efficient, organized layouts and with encouragement. Future studies should empirically validate findings in practice. CLINICAL TRIAL INFORMATION: The Clinical Trials Registration #: NCT06349460.

  PublisherOA PDFDOI

Recent grants

• SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing

  NIH · $714k · 2018–2022

• EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models

  NSF · $315k · 2019–2022

• WildCam: A Privacy Conscious Wearable Eating Detection Camera People will Actually Wear in the Wild

  NIH · $234k · 2021–2024

• SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing

  NIH · $166k · 2018–2022

Frequent coauthors

• Majid Sarrafzadeh

  46 shared

• Bonnie Spring

  22 shared

• Mohammad Pourhomayoun

  California State University Los Angeles

  21 shared

• Haik Kalantarian

  Stanford University

  19 shared

• Josiah Hester

  Georgia Institute of Technology

  18 shared

• Angela Fidler Pfammatter

  University of Tennessee at Knoxville

  14 shared

• Rawan Alharbi

  Northwestern University

  14 shared

• Wenyao Xu

  University at Buffalo, State University of New York

  13 shared

Education

• Doctor of Philosophy (PhD), Computer Science

  University of California Los Angeles

  2015

• Master of Science, Computer Science

  University of California Los Angeles

  2010

• Bachelor of Science, Computer Science

  University of California Los Angeles

  2003