R01DK129843
Project Grant
Overview
Grant Description
Eat: A Reliable Eating Assessment Technology for Free-Living Individuals - Project Summary/Abstract
Overeating and unhealthy eating are often associated with various health risk conditions such as obesity, high blood pressure, and some chronic diseases. To gain a better understanding of overeating and unhealthy eating, researchers often rely on self-reports provided by individuals. Suggestions regarding lifestyle changes are often based on observations from these self-reports. However, it is well known that self-reports can be erroneous and subject to reporting biases. Thus, an objective way to measure eating activity and validate self-reports is necessary.
Recently, there has been growing interest in moving beyond self-reports and monitoring eating activity automatically. To monitor automatically and in real time, researchers have looked at using sensor data from wrist-worn devices, neck-worn devices, or ear-worn devices to automatically detect eating. These devices often enable capturing the eating periods. However, these devices seldom capture images, thus limiting the possibility of visually confirming the consumed food and its quantity. With the increasing popularity of wearable cameras, it is gradually becoming possible to capture eating activities and associated context automatically and without any user intervention. Advances in machine learning enable the automatic extraction of eating-related information from these captured images.
However, wearable cameras often capture more information than necessary, such as capturing bystanders. This unnecessary information capturing reduces participants' willingness to wear the camera. Currently, no camera exists that can capture the eating activity and at the same time limit capturing unnecessary information. Obfuscating the unnecessary information might increase participants' willingness to wear the camera. However, it is unclear if and which obfuscation technique will increase participants' willingness to don the wearable camera and at the same time ensure automatic context determination.
In this project, we will determine the possibility of using machine learning to detect eating in videos and identify the obfuscation technique that can allow detecting the eating activity without collecting unnecessary information. To this end, first, we will develop an activity detection algorithm that will allow detecting the eating activity using data from an IR sensor array and RGB images. Next, we will test various obfuscation methods in a crossover trial and select the best obfuscation method based on the greatest participant acceptability. We will then deploy the eating detection algorithm with the best obfuscation approach on a novel wearable camera that has an infrared sensor array. We will use this camera to test the possibility of detecting eating in a real-world setting.
To validate our algorithm, we will ask people to confirm or refute predicted eating and non-eating moments. We will compare the performance of this algorithm against both real-time user response and 24-hour dietary recall to objectively evaluate the algorithm's performance. Our proposed system will improve current research practices of evaluating dietary intake and pave the way for personalized interventions for behavioral medicine.
Overeating and unhealthy eating are often associated with various health risk conditions such as obesity, high blood pressure, and some chronic diseases. To gain a better understanding of overeating and unhealthy eating, researchers often rely on self-reports provided by individuals. Suggestions regarding lifestyle changes are often based on observations from these self-reports. However, it is well known that self-reports can be erroneous and subject to reporting biases. Thus, an objective way to measure eating activity and validate self-reports is necessary.
Recently, there has been growing interest in moving beyond self-reports and monitoring eating activity automatically. To monitor automatically and in real time, researchers have looked at using sensor data from wrist-worn devices, neck-worn devices, or ear-worn devices to automatically detect eating. These devices often enable capturing the eating periods. However, these devices seldom capture images, thus limiting the possibility of visually confirming the consumed food and its quantity. With the increasing popularity of wearable cameras, it is gradually becoming possible to capture eating activities and associated context automatically and without any user intervention. Advances in machine learning enable the automatic extraction of eating-related information from these captured images.
However, wearable cameras often capture more information than necessary, such as capturing bystanders. This unnecessary information capturing reduces participants' willingness to wear the camera. Currently, no camera exists that can capture the eating activity and at the same time limit capturing unnecessary information. Obfuscating the unnecessary information might increase participants' willingness to wear the camera. However, it is unclear if and which obfuscation technique will increase participants' willingness to don the wearable camera and at the same time ensure automatic context determination.
In this project, we will determine the possibility of using machine learning to detect eating in videos and identify the obfuscation technique that can allow detecting the eating activity without collecting unnecessary information. To this end, first, we will develop an activity detection algorithm that will allow detecting the eating activity using data from an IR sensor array and RGB images. Next, we will test various obfuscation methods in a crossover trial and select the best obfuscation method based on the greatest participant acceptability. We will then deploy the eating detection algorithm with the best obfuscation approach on a novel wearable camera that has an infrared sensor array. We will use this camera to test the possibility of detecting eating in a real-world setting.
To validate our algorithm, we will ask people to confirm or refute predicted eating and non-eating moments. We will compare the performance of this algorithm against both real-time user response and 24-hour dietary recall to objectively evaluate the algorithm's performance. Our proposed system will improve current research practices of evaluating dietary intake and pave the way for personalized interventions for behavioral medicine.
Awardee
Funding Goals
(1) TO PROMOTE EXTRAMURAL BASIC AND CLINICAL BIOMEDICAL RESEARCH THAT IMPROVES THE UNDERSTANDING OF THE MECHANISMS UNDERLYING DISEASE AND LEADS TO IMPROVED PREVENTIONS, DIAGNOSIS, AND TREATMENT OF DIABETES, DIGESTIVE, AND KIDNEY DISEASES. PROGRAMMATIC AREAS WITHIN THE NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES INCLUDE DIABETES, DIGESTIVE, ENDOCRINE, HEMATOLOGIC, LIVER, METABOLIC, NEPHROLOGIC, NUTRITION, OBESITY, AND UROLOGIC DISEASES. SPECIFIC PROGRAMS AREAS OF INTEREST INCLUDE THE FOLLOWING: (A) FOR DIABETES, ENDOCRINE, AND METABOLIC DISEASES AREAS: FUNDAMENTAL AND CLINICAL STUDIES INCLUDING THE ETIOLOGY, PATHOGENESIS, PREVENTION, DIAGNOSIS, TREATMENT AND CURE OF DIABETES MELLITUS AND ITS COMPLICATIONS, NORMAL AND ABNORMAL FUNCTION OF THE PITUITARY, THYROID, PARATHYROID, ADRENAL, AND OTHER HORMONE SECRETING GLANDS, HORMONAL REGULATION OF BONE, ADIPOSE TISSUE, AND LIVER, ON FUNDAMENTAL ASPECTS OF SIGNAL TRANSDUCTION, INCLUDING THE ACTION OF HORMONES, COREGULATORS, AND CHROMATIN REMODELING PROTEINS, HORMONE BIOSYNTHESIS, SECRETION, METABOLISM, AND BINDING, AND ON HORMONAL REGULATION OF GENE EXPRESSION AND THE ROLE(S) OF SELECTIVE RECEPTOR MODULATORS AS PARTIAL AGONISTS OR ANTAGONISTS OF HORMONE ACTION, AND FUNDAMENTAL STUDIES RELEVANT TO METABOLIC DISORDERS INCLUDING MEMBRANE STRUCTURE, FUNCTION, AND TRANSPORT PHENOMENA AND ENZYME BIOSYNTHESIS, AND BASIC AND CLINICAL STUDIES ON THE ETIOLOGY, PATHOGENESIS, PREVENTION, AND TREATMENT OF INHERITED METABOLIC DISORDERS (SUCH AS CYSTIC FIBROSIS). (B) FOR DIGESTIVE DISEASE AND NUTRITION AREAS: GENETICS AND GENOMICS OF THE GI TRACT AND ITS DISEASES, GENETICS AND GENOMICS OF LIVER/PANCREAS AND DISEASES, GENETICS AND GENOMICS OF NUTRITION, GENETICS AND GENOMICS OF OBESITY, BARIATRIC SURGERY, CLINICAL NUTRITION RESEARCH, CLINICAL OBESITY RESEARCH, COMPLICATIONS OF CHRONIC LIVER DISEASE, FATTY LIVER DISEASE, GENETIC LIVER DISEASE, HIV AND LIVER, CELL INJURY, REPAIR, FIBROSIS AND INFLAMMATION IN THE LIVER, LIVER CANCER, LIVER TRANSPLANTATION, PEDIATRIC LIVER DISEASE, VIRAL HEPATITIS AND INFECTIOUS DISEASES, GASTROINTESTINAL AND NUTRITION EFFECTS OF AIDS, GASTROINTESTINAL MUCOSAL AND IMMUNOLOGY, GASTROINTESTINAL MOTILITY, BASIC NEUROGASTROENTEROLOGY, GASTROINTESTINAL DEVELOPMENT, GASTROINTESTINAL EPITHELIAL BIOLOGY, GASTROINTESTINAL INFLAMMATION, DIGESTIVE DISEASES EPIDEMIOLOGY AND DATA SYSTEMS, NUTRITIONAL EPIDEMIOLOGY AND DATA SYSTEMS, AUTOIMMUNE LIVER DISEASE, BILE, BILIRUBIN AND CHOLESTASIS, BIOENGINEERING AND BIOTECHNOLOGY RELATED TO DIGESTIVE DISEASES, LIVER, NUTRITION AND OBESITY, CELL AND MOLECULAR BIOLOGY OF THE LIVER, DEVELOPMENTAL BIOLOGY AND REGENERATION, DRUG-INDUCED LIVER DISEASE, GALLBLADDER DISEASE AND BILIARY DISEASES, EXOCRINE PANCREAS BIOLOGY AND DISEASES, GASTROINTESTINAL NEUROENDOCRINOLOGY, GASTROINTESTINAL TRANSPORT AND ABSORPTION, NUTRIENT METABOLISM, PEDIATRIC CLINICAL OBESITY, CLINICAL TRIALS IN DIGESTIVE DISEASES, LIVER CLINICAL TRIALS, OBESITY PREVENTION AND TREATMENT, AND OBESITY AND EATING DISORDERS. (C) FOR KIDNEY, UROLOGIC AND HEMATOLOGIC DISEASES AREAS: STUDIES OF THE DEVELOPMENT, PHYSIOLOGY, AND CELL BIOLOGY OF THE KIDNEY, PATHOPHYSIOLOGY OF THE KIDNEY, GENETICS OF KIDNEY DISORDERS, IMMUNE MECHANISMS OF KIDNEY DISEASE, KIDNEY DISEASE AS A COMPLICATION OF DIABETES, EFFECTS OF DRUGS, NEPHROTOXINS AND ENVIRONMENTAL TOXINS ON THE KIDNEY, MECHANISMS OF KIDNEY INJURY REPAIR, IMPROVED DIAGNOSIS, PREVENTION AND TREATMENT OF CHRONIC KIDNEY DISEASE AND END-STAGE RENAL DISEASE, IMPROVED APPROACHES TO MAINTENANCE DIALYSIS THERAPIES, BASIC STUDIES OF LOWER URINARY TRACT CELL BIOLOGY, DEVELOPMENT, PHYSIOLOGY, AND PATHOPHYSIOLOGY, CLINICAL STUDIES OF BLADDER DYSFUNCTION, INCONTINENCE, PYELONEPHRITIS, INTERSTITIAL CYSTITIS, BENIGN PROSTATIC HYPERPLASIA, UROLITHIASIS, AND VESICOURETERAL REFLUX, DEVELOPMENT OF NOVEL DIAGNOSTIC TOOLS AND IMPROVED THERAPIES, INCLUDING TISSUE ENGINEERING STRATEGIES, FOR UROLOGIC DISORDERS,RESEARCH ON HEMATOPOIETIC CELL DIFFERENTIATION, METABOLISM OF IRON OVERLOAD AND DEFICIENCY, STRUCTURE, BIOSYNTHESIS AND GENETIC REGULATION OF HEMOGLOBIN, AS WELL AS RESEARCH ON THE ETIOLOGY, PATHOGENESIS, AND THERAPEUTIC MODALITIES FOR THE ANEMIA OF INFLAMMATION AND CHRONIC DISEASES. (2) TO ENCOURAGE BASIC AND CLINICAL RESEARCH TRAINING AND CAREER DEVELOPMENT OF SCIENTISTS DURING THE EARLY STAGES OF THEIR CAREERS. THE RUTH L. KIRSCHSTEIN NATIONAL RESEARCH SERVICE AWARD (NRSA) FUNDS BASIC AND CLINICAL RESEARCH TRAINING, SUPPORT FOR CAREER DEVELOPMENT, AND THE TRANSITION FROM POSTDOCTORAL BIOMEDICAL RESEARCH TRAINING TO INDEPENDENT RESEARCH RELATED TO DIABETES, DIGESTIVE, ENDOCRINE, HEMATOLOGIC, LIVER, METABOLIC, NEPHROLOGIC, NUTRITION, OBESITY, AND UROLOGIC DISEASES. (3) TO EXPAND AND IMPROVE THE SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM. THE SBIR PROGRAM AIMS TO INCREASE AND FACILITATE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO ENHANCE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. (4) TO UTILIZE THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM. THE STTR PROGRAM INTENDS TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Chicago,
Illinois
606114407
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 369% from $701,652 to $3,289,685.
Northwestern University was awarded
EatSense: Advanced Eating Detection Technology for Health Monitoring
Project Grant R01DK129843
worth $3,289,685
from the National Institute of Diabetes and Digestive and Kidney Diseases in August 2021 with work to be completed primarily in Chicago Illinois United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.847 Diabetes, Digestive, and Kidney Diseases Extramural Research.
The Project Grant was awarded through grant opportunity Diet and Physical Activity Assessment Methodology (R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 8/6/25
Period of Performance
8/1/21
Start Date
7/31/26
End Date
Funding Split
$3.3M
Federal Obligation
$0.0
Non-Federal Obligation
$3.3M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01DK129843
Transaction History
Modifications to R01DK129843
Additional Detail
Award ID FAIN
R01DK129843
SAI Number
R01DK129843-4278628661
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NK00 NIH National Institute of Diabetes and Digestive and Kidney Diseases
Funding Office
75NK00 NIH National Institute of Diabetes and Digestive and Kidney Diseases
Awardee UEI
KG76WYENL5K1
Awardee CAGE
01725
Performance District
IL-05
Senators
Richard Durbin
Tammy Duckworth
Tammy Duckworth
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Health and Human Services (075-0884) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,340,905 | 100% |
Modified: 8/6/25