Browsing by Author "McCrory, Megan A."
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Item Body mass index and variability in meal duration and association with rate of eating(Frontiers, 2022) Simon, Stacey L.; Pan, Zhaoxing; Marden, Tyson; Zhou, Wenru; Ghosh, Tonmoy; Hossain, Delwar; Thomas, J. Graham; McCrory, Megan A.; Sazonov, Edward; Higgins, Janine; University of Colorado Anschutz Medical Campus; University of Alabama Tuscaloosa; Brown University; Boston UniversityBackgroundA fast rate of eating is associated with a higher risk for obesity but existing studies are limited by reliance on self-report and the consistency of eating rate has not been examined across all meals in a day. The goal of the current analysis was to examine associations between meal duration, rate of eating, and body mass index (BMI) and to assess the variance of meal duration and eating rate across different meals during the day. MethodsUsing an observational cross-sectional study design, non-smoking participants aged 18-45 years (N = 29) consumed all meals (breakfast, lunch, and dinner) on a single day in a pseudo free-living environment. Participants were allowed to choose any food and beverages from a University food court and consume their desired amount with no time restrictions. Weighed food records and a log of meal start and end times, to calculate duration, were obtained by a trained research assistant. Spearman's correlations and multiple linear regressions examined associations between BMI and meal duration and rate of eating. ResultsParticipants were 65% male and 48% white. A shorter meal duration was associated with a higher BMI at breakfast but not lunch or dinner, after adjusting for age and sex (p = 0.03). Faster rate of eating was associated with higher BMI across all meals (p = 0.04) and higher energy intake for all meals (p < 0.001). Intra-individual rates of eating were not significantly different across breakfast, lunch, and dinner (p = 0.96). ConclusionShorter beakfast and a faster rate of eating across all meals were associated with higher BMI in a pseudo free-living environment. An individual's rate of eating is constant over all meals in a day. These data support weight reduction interventions focusing on the rate of eating at all meals throughout the day and provide evidence for specifically directing attention to breakfast eating behaviors.Item Development and Validation of an Objective, Passive Dietary Assessment Method for Estimating Food and Nutrient Intake in Households in Low- and Middle-Income Countries: A Study Protocol(Oxford University Press, 2020) Jobarteh, Modou L.; McCrory, Megan A.; Lo, Benny; Sun, Mingui; Sazonov, Edward; Anderson, Alex K.; Jia, Wenyan; Maitland, Kathryn; Qiu, Jianing; Steiner-Asiedu, Matilda; Higgins, Janine A.; Baranowski, Tom; Olupot-Olupot, Peter; Frost, Gary; Imperial College London; Boston University; University of Pittsburgh; University of Alabama Tuscaloosa; University of Georgia; University of Ghana; University of Colorado Anschutz Medical Campus; Baylor College of Medicine; United States Department of Agriculture (USDA)Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460).Item Feasibility of the automatic ingestion monitor (AIM-2) for infant feeding assessment: a pilot study among breast-feeding mothers from Ghana(Cambridge University Press, 2022) Cerminaro, Caroline; Sazonov, Edward; McCrory, Megan A.; Steiner-Asiedu, Matilda; Bhaskar, Viprav; Gallo, Sina; Laing, Emma; Jia, Wenyan; Sun, Mingui; Baranowski, Tom; Frost, Gary; Lo, Benny; Anderson, Alex Kojo; University of Georgia; University of Alabama Tuscaloosa; Boston University; University of Ghana; University of Pittsburgh; United States Department of Agriculture (USDA); Baylor College of Medicine; Imperial College LondonObjective: Passive, wearable sensors can be used to obtain objective information in infant feeding, but their use has not been tested. Our objective was to compare assessment of infant feeding (frequency, duration and cues) by self-report and that of the Automatic Ingestion Monitor-2 (AIM-2). Design: A cross-sectional pilot study was conducted in Ghana. Mothers wore the AIM-2 on eyeglasses for 1 d during waking hours to assess infant feeding using images automatically captured by the device every 15 s. Feasibility was assessed using compliance with wearing the device. Infant feeding practices collected by the AIM-2 images were annotated by a trained evaluator and compared with maternal self-report via interviewer-administered questionnaire. Setting: Rural and urban communities in Ghana. Participants: Participants were thirty eight (eighteen rural and twenty urban) breast-feeding mothers of infants (child age <= 7 months). Results: Twenty-five mothers reported exclusive breast-feeding, which was common among those < 30 years of age (n 15, 60 %) and those residing in urban communities (n 14, 70 %). Compliance with wearing the AIM-2 was high (83 % of wake-time), suggesting low user burden. Maternal report differed from the AIM-2 data, such that mothers reported higher mean breast-feeding frequency (eleven v. eight times, P = 0 center dot 041) and duration (18 center dot 5 v. 10 min, P = 0 center dot 007) during waking hours. Conclusion: The AIM-2 was a feasible tool for the assessment of infant feeding among mothers in Ghana as a passive, objective method and identified overestimation of self-reported breast-feeding frequency and duration. Future studies using the AIM-2 are warranted to determine validity on a larger scale.Item Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features(Frontiers, 2021) Chen, Guangzong; Jia, Wenyan; Zhao, Yifan; Mao, Zhi-Hong; Lo, Benny; Anderson, Alex K.; Frost, Gary; Jobarteh, Modou L.; McCrory, Megan A.; Sazonov, Edward; Steiner-Asiedu, Matilda; Ansong, Richard S.; Baranowski, Thomas; Burke, Lora; Sun, Mingui; University of Pittsburgh; Imperial College London; University of Georgia; Boston University; University of Alabama Tuscaloosa; University of Ghana; United States Department of Agriculture (USDA); Baylor College of MedicineMalnutrition, including both undernutrition and obesity, is a significant problemin low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.Item I2N: image to nutrients, a sensor guided semi-automated tool for annotation of images for nutrition analysis of eating episodes(Frontiers, 2023) Ghosh, Tonmoy; McCrory, Megan A.; Marden, Tyson; Higgins, Janine; Anderson, Alex Kojo; Domfe, Christabel Ampong; Jia, Wenyan; Lo, Benny; Frost, Gary; Steiner-Asiedu, Matilda; Baranowski, Tom; Sun, Mingui; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado Denver; University of Colorado Anschutz Medical Campus; University of Georgia; University of Pittsburgh; Imperial College London; University of Ghana; Baylor College of MedicineIntroductionDietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user. MethodsThis paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data). ResultsIn both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N's sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images. DiscussionI2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren't available.Item Improvement of Methodology for Manual Energy Intake Estimation From Passive Capture Devices(Frontiers, 2022) Pan, Zhaoxing; Forjan, Dan; Marden, Tyson; Padia, Jonathan; Ghosh, Tonmoy; Hossain, Delwar; Thomas, J. Graham; McCrory, Megan A.; Sazonov, Edward; Higgins, Janine A.; University of Colorado Anschutz Medical Campus; University of Alabama Tuscaloosa; Brown University; Boston UniversityObjective: To describe best practices for manual nutritional analyses of data from passive capture wearable devices in free-living conditions. Method: 18 participants (10 female) with a mean age of 45 +/- 10 years and mean BMI of 34.2 +/- 4.6 kg/m(2) consumed usual diet for 3 days in a free-living environment while wearing an automated passive capture device. This wearable device facilitates capture of images without manual input from the user. Data from the first nine participants were used by two trained nutritionists to identify sources contributing to inter-nutritionist variance in nutritional analyses. The nutritionists implemented best practices to mitigate these sources of variance in the next nine participants. The three best practices to reduce variance in analysis of energy intake (EI) estimation were: (1) a priori standardized food selection, (2) standardized nutrient database selection, and (3) increased number of images captured around eating episodes. Results: Inter-rater repeatability for EI, using intraclass correlation coefficient (ICC), improved by 0.39 from pre-best practices to post-best practices (0.14 vs 0.85, 95% CI, respectively), Bland-Altman analysis indicated strongly improved agreement between nutritionists for limits of agreement (LOA) post-best practices. Conclusion: Significant improvement of ICC and LOA for estimation of EI following implementation of best practices demonstrates that these practices improve the reproducibility of dietary analysis from passive capture device images in free-living environments.Item Meal Microstructure Characterization from Sensor-Based Food Intake Detection(Frontiers Media, 2017-07-17) Doulah, Abul; Farooq, Muhammad; Yang, Xin; Parton, Jason; McCrory, Megan A.; Higgins, Janine A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado System; University of Colorado Anschutz Medical Campus; University of Colorado DenverTo avoid the pitfalls of self-reported dietary intake, wearable sensors can be used. Many food ingestion sensors offer the ability to automatically detect food intake using time resolutions that range from 23 ms to 8 min. There is no defined standard time resolution to accurately measure ingestive behavior or a meal microstructure. This paper aims to estimate the time resolution needed to accurately represent the microstructure of meals such as duration of eating episode, the duration of actual ingestion, and number of eating events. Twelve participants wore the automatic ingestion monitor (AIM) and kept a standard diet diary to report their food intake in free-living conditions for 24 h. As a reference, participants were also asked to mark food intake with a push button sampled every 0.1 s. The duration of eating episodes, duration of ingestion, and number of eating events were computed from the food diary, AIM, and the push button resampled at different time resolutions (0.1-30s). ANOVA and multiple comparison tests showed that the duration of eating episodes estimated from the diary differed significantly from that estimated by the AIM and the push button (p-value <0.001). There were no significant differences in the number of eating events for push button resolutions of 0.1, 1, and 5 s, but there were significant differences in resolutions of 10-30s (p-value <0.05). The results suggest that the desired time resolution of sensor-based food intake detection should be <= 5 s to accurately detect meal microstructure. Furthermore, the AIM provides more accurate measurement of the eating episode duration than the diet diary.Item Monitoring of Eating Behavior Using Sensor-Based Methods(University of Alabama Libraries, 2024) Hossain, Delwar; Sazonov, EdwardThe essential physiological functions of the human body, including respiration, circulation, physical exertion, and protein synthesis, rely on energy from dietary constituents. Understanding eating behavior is crucial for overall health, as deviations in energy intake can lead to malnutrition-induced weight loss or obesity-related weight gain. Traditionally, dietary intake assessment has relied on self-reporting methods, such as dietary records, 24-hour recalls, and food frequency questionnaires. While these methods help understand relationships between eating behavior and dietary intake, they lack the granularity needed to explore detailed food consumption processes. Therefore, there is a need for innovative solutions that enable objective, precise, and automated monitoring of eating behavior, especially in free-living conditions.This dissertation investigates the application of wearable sensor systems for the automatic monitoring of eating behavior with minimal effort from subjects. First, a systematic review was conducted to identify available technology-driven methods for monitoring eating behavior. Then, a novel, contactless method for detecting and measuring eating behaviors such as chews and bites from eating videos was developed. Then an algorithm was devised to evaluate and compare different sensor modalities for identifying eating behavior, specifically focusing on chewing and chewing strength measurement. Four sensor modalities--Ear Canal Pressure Sensor, Piezoresistive Bend Sensor, Piezoelectric Strain Sensor, and EMG Sensor--were assessed. Results indicated comparable efficacy across all four systems in identifying chewing and chewing strength.Next, a novel Ear Canal Pressure Sensor system was explored for monitoring eating behavior, particularly chewing, in free-living environments. The findings demonstrated accurate detection and estimation of chewing in both controlled and free-living settings. Finally, a machine learning model to estimate energy intake (EI) from food intake using sensor-captured eating behavior features was developed and evaluated in free-living settings. The results highlight the efficacy of the sensor-based EI model and the potential for improved accuracy by leveraging image assistance and automatic food item detection.In conclusion, this research advances eating behavior monitoring using wearable sensor technologies. The findings hold promise for personalized nutrition interventions and mark a significant step forward in the objective assessment of eating habits.Item A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation(MDPI, 2022) Jia, Wenyan; Ren, Yiqiu; Li, Boyang; Beatrice, Britney; Que, Jingda; Cao, Shunxin; Wu, Zekun; Mao, Zhi-Hong; Lo, Benny; Anderson, Alex K.; Frost, Gary; McCrory, Megan A.; Sazonov, Edward; Steiner-Asiedu, Matilda; Baranowski, Tom; Burke, Lora E.; Sun, Mingui; University of Pittsburgh; Imperial College London; University of Georgia; Boston University; University of Alabama Tuscaloosa; University of Ghana; Baylor College of Medicine; United States Department of Agriculture (USDA)Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.Item Reduction of Energy Intake Using Just-in-Time Feedback from a Wearable Sensor System(Wiley, 2017) Farooq, Muhammad; McCrory, Megan A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston UniversityObjective: This work explored the potential use of a wearable sensor system for providing just-in-time (JIT) feedback on the progression of a meal and tested its ability to reduce the total food mass intake. Methods: Eighteen participants consumed three meals each in a lab while monitored by a wearable sensor system capable of accurately tracking chew counts. The baseline visit was used to establish the self-determined ingested mass and the associated chew counts. Real-time feedback on chew counts was provided in the next two visits, during which the target chew count was either the same as that at baseline or the baseline chew count reduced by 25% (in randomized order). The target was concealed from the participant and from the experimenter. Nonparametric repeated-measures ANOVAs were performed to compare mass of intake, meal duration, and ratings of hunger, appetite, and thirst across three meals. Results: JIT feedback targeting a 25% reduction in chew counts resulted in a reduction in mass and energy intake without affecting perceived hunger or fullness. Conclusions: JIT feedback on chewing behavior may reduce intake within a meal. This system can be further used to help develop individualized strategies to provide JIT adaptive interventions for reducing energy intake.Item Reproducibility of Dietary Intake Measurement From Diet Diaries, Photographic Food Records, and a Novel Sensor Method(Frontiers, 2020) Fontana, Juan M.; Pan, Zhaoxing; Sazonov, Edward S.; McCrory, Megan A.; Graham Thomas, J.; McGrane, Kelli S.; Marden, Tyson; Higgins, Janine A.; Universidad Nacional Rio Cuarto; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); University of Colorado Anschutz Medical Campus; Colorado School of Public Health; University of Alabama Tuscaloosa; Boston University; Brown UniversityObjective:No data currently exist on the reproducibility of photographic food records compared to diet diaries, two commonly used methods to measure dietary intake. Our aim was to examine the reproducibility of diet diaries, photographic food records, and a novel electronic sensor, consisting of counts of chews and swallows using wearable sensors and video analysis, for estimating energy intake. Method:This was a retrospective analysis of data from a previous study, in which 30 participants (15 female), aged 29 +/- 12 y and having a BMI of 27.9 +/- 5.5, consumed three identical meals on different days. Four different methods were used to estimate total mass and energy intake on each day: (1) weighed food record; (2) photographic food record; (3) diet diary; and (4) novel mathematical model based on counts of chews and swallows (CCS models) obtained via the use of electronic sensors and video monitoring system. The study staff conducted weighed food records for all meals, took pre- and post-meal photographs, and ensured that diet diaries were completed by participants at the end of each meal. All methods were compared against the weighed food record, which was used as the reference method. Results:Reproducibility was significantly different between the diet diary and photographic food record for total energy intake (p= 0.004). The photographic record had greater reproducibility vs. the diet diary for all parameters measured. For total energy intake, the novel sensor method exhibited good reproducibility (repeatability coefficient (RC) of 59.9 (45.9, 70.4), which was better than that for the diet diary [RC = 79.6 (55.5, 103.3)] but not as repeatable as the photographic method [RC = 43.4 (32.1, 53.9)]. Conclusion:Photographic food records offer superior precision to the diet diary and, therefore, would be valuable for longitudinal studies with repeated measures of dietary intake. A novel electronic sensor also shows promise for the collection of longitudinal dietary intake data.Item The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device(Frontiers, 2023) Breit, Matthew; Padia, Jonathan; Marden, Tyson; Forjan, Dan; Pan, Zhaoxing; Zhou, Wenru; Ghosh, Tonmoy; Thomas, Graham; McCrory, Megan A.; Sazonov, Edward; Higgins, Janine; University of Colorado Anschutz Medical Campus; University of Alabama Tuscaloosa; Brown University; Lifespan Health Rhode Island; Miriam Hospital; Boston UniversityIntroductionThe aim of this feasibility and proof-of-concept study was to examine the use of a novel wearable device for automatic food intake detection to capture the full range of free-living eating environments of adults with overweight and obesity. In this paper, we document eating environments of individuals that have not been thoroughly described previously in nutrition software as current practices rely on participant self-report and methods with limited eating environment options. MethodsData from 25 participants and 116 total days (7 men, 18 women, M-age = 44 +/- 12 years, BMI 34.3 +/- 5.2 kg/mm(2)), who wore the passive capture device for at least 7 consecutive days (>= 12h waking hours/d) were analyzed. Data were analyzed at the participant level and stratified amongst meal type into breakfast, lunch, dinner, and snack categories. Out of 116 days, 68.1% included breakfast, 71.5% included lunch, 82.8% included dinner, and 86.2% included at least one snack. ResultsThe most prevalent eating environment among all eating occasions was at home and with one or more screens in use (breakfast: 48.1%, lunch: 42.2%, dinner: 50%, and snacks: 55%), eating alone (breakfast: 75.9%, lunch: 89.2%, dinner: 74.3%, snacks: 74.3%), in the dining room (breakfast: 36.7%, lunch: 30.1%, dinner: 45.8%) or living room (snacks: 28.0%), and in multiple locations (breakfast: 44.3%, lunch: 28.8%, dinner: 44.8%, snacks: 41.3%). DiscussionResults suggest a passive capture device can provide accurate detection of food intake in multiple eating environments. To our knowledge, this is the first study to classify eating occasions in multiple eating environments and may be a useful tool for future behavioral research studies to accurately codify eating environments.Item Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor(Nature Portfolio, 2019) Yang, Xin; Doulah, Abul; Farooq, Muhammad; Parton, Jason; McCrory, Megan A.; Higgins, Janine A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado Anschutz Medical Campus; University of Colorado DenverAccurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean +/- standard deviation) absolute percentage errors of 25.2% +/- 18.9% and 30.1% +/- 33.8%, respectively, and mean +/- standard deviation estimation errors of -17.7 +/- 226.9 g and -6.1 +/- 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.Item Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment(MDPI, 2019) Farooq, Muhammad; Doulah, Abul; Parton, Jason; McCrory, Megan A.; Higgins, Janine A.; Sazonov, Edward; University of Alabama Tuscaloosa; Boston University; University of Colorado Anschutz Medical CampusVideo observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants' food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (+/- standard deviation (STD)) kappa value of 0.74 (+/- 0.02) and for food intake annotation the average kappa (Light's kappa) of 0.82 (+/- 0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (+/- 0.10) and 0.78 (+/- 0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters' annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.Item A wearable sensor system for automatic detection, characterization and modification of eating behaviour(University of Alabama Libraries, 2015) Farooq, Muhammad; Sazonov, Edward; University of Alabama TuscaloosaFood intake is the main source of energy and nutrients required to maintain life. The study of food intake patterns and ingestive behavior is critical to human health, as inadequate or excessive energy intake may result in medical conditions such as a decrease in weight or malnutrition, or increase in weight and obesity respectively. Monitoring of ingestive behavior is also important in understanding food intake patterns which contribute to the development of eating disorders such as anorexia nervosa, bulimia, and binge eating. Traditionally, ingestive behavior is assessed and monitored through self-reporting methods such as dietary records, 24hrs recall, and food frequency questionnaire, etc. However, these methods suffer severely from underreporting which may be as high as 50%. Thus, there is a need for the development of solutions for objective, accurate and automatic monitoring of the ingestive behavior of individuals, especially under free-living conditions. This work investigates the use of wearable sensor system for automatic detection, characterization and modification of the eating behavior of individuals with minimal or no conscious effort from the individuals. Automatic detection of food intake is proposed via monitoring of chewing and swallowing associated with food intake. Chewing monitoring is performed by using a piezoelectric strain sensor. A study was performed for food intake detection via chewing monitoring in free-living conditions for 24 hrs where chewing was captured with a piezoelectric strain sensor. Swallowing was monitored by using Electroglottography (EGG) measurement for monitoring of ingestive behavior during ad-libitum food intake in a controlled setting. This work also presents a new sensor system for which can accurately detect eating episodes in the presence of excessive ambulation. Research suggests that modifying the chewing behavior might be helpful in reducing the energy intake. This work further explores the potential use of the presented wearable sensor system to provide just-in-time feedback on the progression (based on total chew counts) of a meal and test its ability to reduce the total mass intake.Item A wearable sensor system for automatic food intake detection and energy intake estimation in humans(University of Alabama Libraries, 2018) Doulah, Abul Barkat Mollah Sayeed Ud; Sazonov, Edward; University of Alabama TuscaloosaAccurate measurement and estimation of energy intake (EI) are important for the understanding of energy balance and body weight dynamics. Food is the primary source of energy in the body. Therefore, the objective monitoring of food intake patterns and eating behavior is necessary, as excessive EI leads to medical conditions such as obesity and overweight. Traditionally, self-report methods (e.g. food records, 24-hour recall) have been used for EI measurement. Most of these methods rely on a participant’s own declaration in one form or another and suffer from misreporting of EI. To lessen the misreporting problems, various methods have been proposed ranging from image-assisted estimation to wearable on-body sensors, each with its own strengths and limitations. While some of the methods show great promise under certain circumstances, objective, accurate and cost-efficient methods for estimating of EI are yet to be developed. Towards automatic food intake detection, this dissertation first explores the desired time resolution of sensor-based food intake detection to characterize meal microstructure. This dissertation then investigates a wearable sensor system for automatic food intake detection, microstructure parameter estimation, passive image capture, and EI estimation. The automatic food intake detection with this system is developed by monitoring chewing activity associated with ingestion. Along with an accelerometer sensor, a novel chewing sensor is introduced to capture chewing information and detect food intake events. A wearable camera, capturing passive images in 15sec intervals, is used to acquire food and non-food images. A visual review of the food intake images is performed to identify food items, estimate portion size and validate that food was consumed. A study was performed with participants wearing the wearable sensor system (Automatic Ingestion Monitor, AIM-2) for 24h in pseudo-free-living and 24h in a free-living environment. Classification models were developed for automatic food intake detection. The estimation of chew counts was obtained using the new chewing sensor which was not adhesively attached to the body. The dissertation further presents an EI estimation method using both sensor-extracted features and image estimated portion size. Results suggest the potential of the wearable system to estimate EI in a free-living environment.