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

> **NIH NIH R03** · NORTHWESTERN UNIVERSITY · 2022 · $117,442

## Abstract

Project Summary
Behavioral interventions for weight loss include self-monitoring and self-report of diet. However, few people adhere to
self-monitoring because they believe it will be a burden. For those who do adhere, biased reporting leads to poor
accuracy. Self-report measures have helped us understand contextual factors of problematic eating behaviors, but we
cannot validate such relationships to dietary intake and weight because of unreliable reporting. Therefore, we need an
objective way to validate self-report measures. Automated self-monitoring, however, can lead to high eating false alarm
detection rates (calling something eating when it is not eating). It also requires participants to push buttons during the
start and end of a meal. Passive and unobtrusive ways to capture images of food intake would improve accuracy of
detection, avoid the need for a person to remember what they ate, and limit bias based on what participants believe to
be socially desirable. Such methods could also ease the self-monitoring burden, decrease errors associated with self-
report measures, and lay the foundation for understanding when and how problematic eating behaviors occur.
Image capture using wearable cameras may help us better understand obesity and its context (that is, the situation in
which eating occurs). However, privacy and ethical concerns of bystanders whose images are taken are a significant
barrier. Currently there is no privacy-preserving camera that participants are both willing to wear and that provides
meaningful information on food intake and context associated with problematic eating behaviors. Several methods
exist, known as obfuscation, that can filter unnecessary information in the scene. However, we do not know which
method is most acceptable to a person wearing the device in everyday life that would encourage greatest wear-time.
Our project aims to determine which method is best for preserving privacy while providing enough information to
understand eating behaviors and their context. To do this, we will observe participants in their everyday life, with
special attention to eating behaviors. We will use these new image capture techniques to help understand eating
behaviors associated with obesity.
First, we will select the best obfuscation method by testing the most well-known obfuscation methods in a cross-over
trial to identify which method has the greatest participant acceptability and feasibility (compared with no obfuscation).
We will test a novel wearable camera with an infrared sensor (allows us to determine objects that are near vs. far in the
camera) with 3 obfuscation methods in real-world settings, including a cartooning gaming–based method. We will then
select the obfuscation method that increases wear time and design an eating algorithm around it. Using this algorithm
we will assess our ability to capture behaviorally meaningful context from the obfuscated image, such as whether
people are eating alone or not, at home,...

## Key facts

- **NIH application ID:** 10336451
- **Project number:** 5R03DK127128-02
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Nabil Alshurafa
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $117,442
- **Award type:** 5
- **Project period:** 2021-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10336451

## Citation

> US National Institutes of Health, RePORTER application 10336451, WildCam: A Privacy Conscious Wearable Eating Detection Camera People will Actually Wear in the Wild (5R03DK127128-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10336451. Licensed CC0.

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