Search

To date, nutritional epidemiology has relied heavily on relatively weak methods including simple observational designs and substandard measurements. Despite low internal validity and other sources of bias, claims of causality are made commonly in this literature. Nutritional epidemiology investigations can be improved through greater scientific rigor and adherence to scientific reporting commensurate with research methods used. Some commentators advocate jettisoning nutritional epidemiology entirely, perhaps believing improvements are impossible. Still others support only normative refinements. But neither abolition nor minor tweaks are appropriate. Nutritional epidemiology, in its present state, offers utility, yet also needs marked, reformational renovation. Changing the status quo will require ongoing, unflinching scrutiny of research questions, practices, and reporting—and a willingness to admit that “good enough” is no longer good enough. As such, a workshop entitled “Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal and defense of the status quo” was held from July 15 to August 14, 2020. This virtual symposium focused on: (1) Stronger Designs, (2) Stronger Measurement, (3) Stronger Analyses, and (4) Stronger Execution and Reporting. Participants from several leading academic institutions explored existing, evolving, and new better practices, tools, and techniques to collaboratively advance specific recommendations for strengthening nutritional epidemiology.

This study evaluated feasibility and acceptability of adding energy balance modeling displayed on weight graphs combined with a wrist-worn bite counting sensor against a traditional online behavioral weight loss program. Adults with a BMI of 27–45 kg/m2 (83.3% women) were randomized to receive a 12-week online behavioral weight loss program with 12 weeks of continued contact (n = 9; base program), the base program plus a graph of their actual and predicted weight change based on individualized physiological parameters (n = 7), or the base program, graph, and a Bite Counter device for monitoring and limiting eating (n = 8). Participants attended weekly clinic weigh-ins plus baseline, midway (12 weeks), and study culmination (24 weeks) assessments of feasibility, acceptability, weight, and behavioral outcomes. In terms of feasibility, participants completed online lessons (M = 7.04 of 12 possible lessons, SD = 4.02) and attended weigh-ins (M = 16.81 visits, SD = 7.24). Six-month retention appears highest among nomogram participants, and weigh-in attendance and lesson completion appear highest in Bite Counter participants. Acceptability was sufficient across groups. Bite Counter use (days with ≥ 2 eating episodes) was moderate (47.8%) and comparable to other studies. Participants lost 4.6% ± 4.5 of their initial body weight at 12 weeks and 4.5% ± 5.8 at 24 weeks. All conditions increased their total physical activity minutes and use of weight control strategies (behavioral outcomes). Although all groups lost weight and the study procedures were feasible, acceptability can be improved with advances in the technology. Participants were satisfied with the online program and nomograms, and future research on engagement, adherence, and integration with other owned devices is needed. ClinicalTrials.gov Identifier: NCT02857595

In this work, we describe a new method to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts a few seconds. Previous works have detected these gestures individually and then aggregated them to identify meals. The novelty of our approach is that we analyze a much longer window (0.5–15 min) using a convolutional neural network. Longer windows can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We test our methods on the public Clemson all-day dataset, which consists of 354 recordings containing 1063 eating episodes. We found that accuracy at detecting eating increased by 15% in ≥4 min windows compared to ≤15 s windows. Using a 6 min window, we detected 89% of eating episodes, with 1.7 false positives for every true positive (FP/TP). These are the best results achieved to date on this dataset.

BACKGROUND Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT. OBJECTIVE The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions. METHODS Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment. RESULTS The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing. CONCLUSIONS This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss). CLINICALTRIAL ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/33568

BACKGROUND: Mobile dietary self-monitoring methods allow for objective assessment of adherence to self-monitoring; however, the best way to define self-monitoring adherence is not known. OBJECTIVE: The objective was to identify the best criteria for defining adherence to dietary self-monitoring with mobile devices when predicting weight loss. DESIGN: This was a secondary data analysis from two 6-month randomized trials: Dietary Intervention to Enhance Tracking with Mobile Devices (n=42 calorie tracking app or n=39 wearable Bite Counter device) and Self-Monitoring Assessment in Real Time (n=20 kcal tracking app or n=23 photo meal app). PARTICIPANTS/SETTING: Adults (n=124; mean body mass index = 34.7±5.6) participated in one of two remotely delivered weight-loss interventions at a southeastern university between 2015 and 2017. INTERVENTION: All participants received the same behavioral weight loss information via twice-weekly podcasts. Participants were randomly assigned to a specific diet tracking method. MAIN OUTCOME MEASURES: Seven methods of tracking adherence to self-monitoring (eg, number of days tracked, and number of eating occasions tracked) were examined, as was weight loss at 6 months. STATISTICAL ANALYSES PERFORMED: Linear regression models estimated the strength of association (R(2)) between each method of tracking adherence and weight loss, adjusting for age and sex. RESULTS: Among all study completers combined (N=91), adherence defined as the overall number of days participants tracked at least two eating occasions explained the most variance in weight loss at 6 months (R(2)=0.27; P

Background This study builds on previous research that seeks to estimate kilocalorie intake through microstructural analysis of eating behaviors. As opposed to previous methods, which used a static, individual-based measure of kilocalories per bite, the new method incorporates time- and food-varying predictors. A measure of kilocalories per bite (KPB) was estimated using between- and within-subjects variables. Objective The purpose of this study was to examine the relationship between within-subjects and between-subjects predictors and KPB, and to develop a model of KPB that improves over previous models of KPB. Within-subjects predictors included time since last bite, food item enjoyment, premeal satiety, and time in meal. Between-subjects predictors included body mass index, mouth volume, and sex. Participants/setting Seventy-two participants (39 female) consumed two random meals out of five possible meal options with known weights and energy densities. There were 4,051 usable bites measured. Main outcome measures The outcome measure of the first analysis was KPB. The outcome measure of the second analysis was meal-level kilocalorie intake, with true intake compared to three estimation methods. Statistical analyses performed Multilevel modeling was used to analyze the influence of the seven predictors of KPB. The accuracy of the model was compared to previous methods of estimating KPB using a repeated-measured analysis of variance. Results All hypothesized relationships were significant, with slopes in the expected direction, except for body mass index and time in meal. In addition, the new model (with nonsignificant predictors removed) improved over earlier models of KPB. Conclusions This model offers a new direction for methods of inexpensive, accurate, and objective estimates of kilocalorie intake from bite-based measures.

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B = -0.16, p .05), were shorter (B = -0.54, p .05), or had a slower rate (B = 1.27, p .001). Statistically significant interactions between eating characteristics (Bs = -0.30 to -0.08, ps .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. CLINICAL TRIALS REGISTRY NUMBER: NCT03739151.

This paper describes a novel approach of segmenting and classifying eating gestures from wrist motion using a deep learning neural network. It is inspired by the approach of fully-convolutional neural networks in the task of image segmentation. Our idea is to segment 1D gestures the same way 2D image regions are segmented, by treating each inertial measurement unit (IMU) datum like a pixel. The novelty of our approach lies in training a neural network to recognize data points that describe an eating gesture just like it would be trained to recognize pixels describing an image region. The data for this research is known as the Clemson Cafeteria Dataset. It was collected from 276 participants that ate an unscripted meal at the Harcombe Dining Hall at Clemson University. Each meal consisted of 1 - 4 courses, and 488 such recordings were used for the experiments described in this paper. Sensor readings consist of measurements taken by an accelerometer (x, y, z) and a gyroscope (yaw, pitch, roll). A total of 51,614 unique gestures associated with different activities commonly seen during a meal were identified by 18 trained raters. Our neural network classifier recognized an average of 79.7% of ‘bite’ and 84.7% of ‘drink’ gestures correctly per meal. Overall 77.7% of all gestures were recognized correctly on average per meal. This indicates that a deep learning model can successfully be used to segment eating gestures from a time series recording of IMU data using a technique similar to pixel segmentation within an image.

Self-efficacy (SE) and information processing (IP) may be important constructs to target when designing mHealth interventions for weight loss. The goal of this study was to examine the relationship between SE and IP with weight loss at six-months as part of the Dietary Interventions Examining Tracking with mobile study, a six-month randomized trial with content delivered remotely via twice-weekly podcasts. Participants were randomized to self-monitor their diet with either a mobile app (n = 42) or wearable Bite Counter device (n = 39). SE was assessed using the Weight Efficacy Life-Style Questionnaire and the IP variables assessed included user control, cognitive load, novelty, elaboration. Regression analysis examined the relationship between weight loss, SE change & IP at six months. Results indicate that elaboration was the strongest predictor of weight loss (ß =−0.423, P = 0.011) among all SE & IP variables and that for every point increase in elaboration, participants lost 0.34 kg body weight.

The universal eating monitor (UEM) is a table-embedded scale used to measure grams consumed over time while a person eats. It has been used in laboratory settings to test the effects of anorectic drugs and behavior manipulations such as slowing eating, and to study relationships between demographics and body weight. However, its use requires restricted conditions on the foods consumed and behaviors allowed during eating in order to simplify analysis of the scale data. Individual bites can only be measured when the only interaction with the scale is to carefully remove a single bite of food, consume it fully, and wait a minimum amount of time before the next bite. Other interactions are prohibited such as stirring and manipulating foods, retrieving or placing napkins or utensils on the scale, and in general anything that would change the scale weight that was not related to the consumption of an individual bite. This paper describes a new algorithm that can detect and measure the weight or individual bites consumed during unrestricted eating. The algorithm works by identifying time periods when the scale weight is stable, and then, analyzing the surrounding weight changes. The series of preceding and succeeding weight changes is compared against patterns for single food bites, food mass bites, and drink bites to determine if a scale interaction is due to a bite or some other activity. The method was tested on 271 subjects, each eating a single meal in a cafeteria setting. A total of 24 101 bites were manually annotated in synchronized videos to establish ground truth as to the true, false, and missed detections of bites. Our algorithm correctly detected and weighed approximately 39% of bites with approximately one false positive (FP) per ten actual bites. The improvement compared to the UEM is approximately three times the number of true detections and a 90% reduction in the number of FPs. Finally, an analysis of bites that could not be weighed compared to those that could be weighed revealed no statistically significant difference in average weight. These results suggest that our algorithm could be used to conduct studies using a table scale outside of laboratory or clinical settings and with unrestricted eating behaviors.

ObjectivesBehavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.e., eating that deviates from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it can be difficult to assess. We propose to study lapses using a multi-method approach, which allows us to identify objectively-measured characteristics of lapse behavior (e.g., eating rate, duration), examine the association between lapse and weight change, and estimate nutrition composition of lapse.MethodWe are recruiting participants (n = 40) with overweight/obesity to enroll in a 24-week BOT. Participants complete biweekly 7-day ecological momentary assessment (EMA) to self-report on eating behavior, including dietary lapses. Participants continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Participants complete 24-hour dietary recalls via structured interview at 6-week intervals to measure the composition of all food and beverages consumed.ResultsWhile data collection for this trial is still ongoing, we present data from three pilot participants who completed EMA and wore the ActiGraph to illustrate the feasibility, benefits, and challenges of this work.ConclusionThis protocol will be the first multi-method study of dietary lapses in BOT. Upon completion, this will be one of the largest published studies of passive eating detection and EMA-reported lapse. The integration of EMA and passive sensing to characterize eating provides contextually rich data that will ultimately inform a nuanced understanding of lapse behavior and enable novel interventions. Trial registration: Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151

Conclusions regarding bite count rates and body mass index (BMI) in free-living populations have primarily relied on self-report. The objective of this exploratory study was to compare the relationship between BMI and bite counts measured by a portable sensor called the Bite Counter in free-living populations and participants eating in residence. Two previously conducted studies were analyzed for relationships between BMI and sensor evaluated bite count/min, and meal duration. Participants from the first study (N = 77) wore the bite counter in a free-living environment for a continuous period of 14 days. The second study (N = 214) collected bite count/min, meal duration, and total energy intake in participants who consumed one meal in a cafeteria. Linear regression was applied to examine relationships between BMI and bite count/min. There was no significant correlation in the free-living participants average bite counts per second and BMI (R2 = 0.03, p = 0.14) and a significant negative correlation in the cafeteria participants (R 2 = 0.04, p = 0.03) with higher bite count rates observed in lean versus obese participants. There was a significant correlation between average meal duration and BMI in the free-living participants (R 2 = 0.08, p = 0.01). Total energy intake in the cafeteria participants was also significantly correlated to meal duration (R 2 = 0.31, p

This paper describes a study to test the accuracy of a method that tracks wrist motion during eating to detect and count bites. The purpose was to assess its accuracy across demographic (age, gender, and ethnicity) and bite (utensil, container, hand used, and food type) variables. Data were collected in a cafeteria under normal eating conditions. A total of 271 participants ate a single meal while wearing a watch-like device to track their wrist motion. A video was simultaneously recorded of each participant and subsequently reviewed to determine the ground truth times of bites. Bite times were operationally defined as the moment when food or beverage was placed into the mouth. Food and beverage choices were not scripted or restricted. Participants were seated in groups of 2-4 and were encouraged to eat naturally. A total of 24 088 bites of 374 different food and beverage items were consumed. Overall the method for automatically detecting bites had a sensitivity of 75% with a positive predictive value of 89%. A range of 62-86% sensitivity was found across demographic variables with slower eating rates trending toward higher sensitivity. Variations in sensitivity due to food type showed a modest correlation with the total wrist motion during the bite, possibly due to an increase in head-toward-plate motion and decrease in hand-toward-mouth motion for some food types. Overall, the findings provide the largest evidence to date that the method produces a reliable automated measure of intake during unrestricted eating.

The relationship between the occurrence of simulator sickness (SS) and varying latency in a helmet-mounted display (HMD) was explored in this study. Previous work has always considered latency to be a constant. The aim of this research was to determine if a latency that varied over time would impact the experience of SS for HMD users. An object location task was used while viewing real, live video scenes via HMD. A planned comparisons approach was utilized with four experimental conditions, 2 of them having constant latency (0 ms added to system baseline, 200 ms added to system baseline) and 2 of them having sinusoidally varying latency (100 ms amplitude at 0.2 Hz frequency, and 20–100 ms varying amplitude at 0.2 Hz frequency). These conditions allowed for the assessment of the effects of constant latency vs. varying latency on the experience of SS. The results indicated that a varying latency is associated with greater experience of SS among HMD users than constant latency. Results also indicated, as has other recent research, that added constant latency on its own does not appear to be associated with the experience of higher levels of SS in an HMD.

The goal of this study was to examine the usability and feasibility of the mobile Bite Counter (a watch-like device that detects when a user consumes food or beverage) and the impact of weekly behavioral challenges on diet and physical activity outcomes. Overweight (mean BMI 31.1±4.9 kg/m2) adults (n=12) were recruited to participate in a four-week study to test both the usability and feasibility of using the device as part of a behavioral weight loss intervention. Participants were instructed to self-monitor number of bites/day using the Bite Counter, attend weekly group sessions, and listen to weekly podcasts. Participants were given weekly challenges: use a daily bite limit goal (wk1), turn off Bite Counter when fruits/vegetables are consumed (wk2), self-monitor kilocalories vs. bites (wk3), and receive a 10 bites/day bonus for every 30 minutes of exercise (wk4). Participants lost a mean of −1.2±1.3 kg. Only the wk3 challenge produced significant differences in kcal change (wk3 1302±120 kcal/day vs. baseline 2042±302 kcal/d, P

Our study investigated the relationship between BMI and bite size in a cafeteria setting. Two hundred and seventy one participants consumed one meal each. Participants were free to select any food provided by the cafeteria and could return for additional food as desired. Bite weights were measured with a table embedded scale. Data were analyzed with ANOVAs, regressions, Kolmogorov-Smirnov tests, and a repeated measures general linear model for quartile analysis. Obese participants were found to take larger bites than both normal (P=0.002) and overweight participants (p=0.017). Average bite size increased by 0.20 g per point increase in BMI. Food bites and drink bites were analyzed individually, showing 0.11 g/BMI and 0.23 g/BMI slopes, respectively. Quartiles of bites were also analyzed, and a significant interaction was found between normal and obese participants (p=0.034) such that the lower two quartiles were similar, but the upper two quartiles showed an increase in bite size for obese participants. The source of these effects could be the result of a combination of several uncontrolled factors.

In the original article, the authors neglected to explicitly state prior to presentation of results for the as-cast water-quenched (ACWQ) plus tempered coupons from the November, 2012 (Y2012) fade campaign that all reported velocities were measured prior to tempering, as the tempering was necessary only to facilitate the sectioning of those coupons by conventional band saw for their metallographic analysis. The tempering of such ACWQ coupons at 482 °C for 48 h raises their velocities by ~ 100 m/s.

Ultrawideband signals present new opportunities for indoor position tracking due to their ability to operate in non-line-of-sight conditions. However, walls and other normal indoor infrastructure cause a systemic spatial warp in the measurements. In this paper, we describe methods to manually build a map of the measurement noise and use it in a particle filter framework to improve measurement accuracy. We tested our methods in two facilities, collecting over 5.6 million measurements. Evidence shows that the measurement noise is multimodal, location dependent, stable over time, and locally similar. Using the map in a particle filter framework, we demonstrate an average improvement in measurement accuracy of 30%.

Background Smartphone photography and crowdsourcing feedback could reduce participant burden for dietary self-monitoring. Objectives To assess if untrained individuals can accurately crowdsource diet quality ratings of food photos using the Traffic Light Diet (TLD) approach. Methods Participants were recruited via Amazon Mechanical Turk and read a one-page description on the TLD. The study examined the participant accuracy score (total number of correctly categorized foods as red, yellow, or green per person), the food accuracy score (accuracy by which each food was categorized), and if the accuracy of ratings increased when more users were included in the crowdsourcing. For each of a range of possible crowd sizes ( n = 15, n = 30, etc.), 10,000 bootstrap samples were drawn and a 95% confidence interval (CI) for accuracy constructed using the 2.5th and 97.5th percentiles. Results Participants ( n = 75; body mass index 28.0 ± 7.5; age 36 ± 11; 59% attempting weight loss) rated 10 foods as red, yellow, or green. Raters demonstrated high red/yellow/green accuracy (>75%) examining all foods. Mean accuracy score per participant was 77.6 ± 14.0%. Individual photos were rated accurately the majority of the time (range = 50%–100%). There was little variation in the 95% CI for each of the five different crowd sizes, indicating that large numbers of individuals may not be needed to accurately crowdsource foods. Conclusions Nutrition-novice users can be trained easily to rate foods using the TLD. Since feedback from crowdsourcing relies on the agreement of the majority, this method holds promise as a low-burden approach to providing diet-quality feedback.

Physiological compliance (PC) refers to the correlation between physiological measures of team members over time. The goals of this study were to examine ways of measuring PC in heart rate variability (HRV) data and the relationship between PC and team performance. Teams were tasked with entering both real and simulated rooms and “shooting” individuals with a weapon and identifying individuals without a weapon. The linear correlation and directional agreement PC methods were shown to be the most sensitive to differences in performance, with greater PC being associated with better performance. The correlation method when applied to a measure of respiratory sinus arrhythmia (RSA) revealed a significant difference between high and low performers ( t [8] = −2.31, p = 0.03) and the directional agreement applied to inter-beat-intervals and RSA revealed trend-level differences ( t [4.62] = −1.86, p = 0.06 and t [8] = −1.68, p = 0.07). These results suggest that PC may have merit for predicting team performance.

Heart rate variability (HRV) is traditionally analyzed while a subject is in a controlled environment, such as at rest in a clinic, where it can be used as a medical indicator. This paper concerns analyzing HRV outside of controlled environments, such as on an actively moving person. We describe automated methods for inter-heartbeat interval (IBI) error detection and correction. We collected 124,998 IBIs from 18 subjects, undergoing a variety of active motions, for use in evaluating our methods. Two human graders manually labeled each IBI, evaluating 10% of the IBIs as having an error, which is a far greater error percentage than has been examined in any previous study. Our automated method had a 96% agreement rate with the two human graders when they themselves agreed, with a 49% rate of matching specific error corrections and a 0.01% false alarm rate.

New technologies are emerging that may help individuals engage in healthier eating behaviors. One paradigm to test the efficacy of a technology is to determine its effect relative to environment cues that are known to cause individuals to overeat.The purpose of this work was to independently investigate two questions: How does the presence of a technology that provides bite count feedback alter eating behavior? and, How does the presence of a technology that provides bite count feedback paired with a goal alter eating behavior?Two studies investigated these research questions. The first study tested the effects of a large and small plate crossed with the presence or absence of a device that provided bite count feedback on intake. The second study tested the effects of a bite count goal with bite count feedback, again crossed with plate size, on intake. Both studies used a 2×2 between-subjects design.In the first study, 94 subjects (62 women aged 19.0±1.6 years with body mass index [BMI] 23.04±3.6) consumed lunch in a laboratory. The second study examined 99 subjects (56 women aged 18.5±1.5 years with BMI 22.73±2.70) under the same conditions.In both studies subjects consumed a single-course meal, using either a small or large plate. In the first study participants either wore or did not wear an automated bite counting device. In the second study all participants wore the bite counting device and were given either a low bite count goal (12 bites) or a high bite count goal (22 bites).Effect of plate size, feedback, and goal on consumption (grams) and number of bites taken were assessed using 2×2 analyses of variance. As adjunct measures, the effects of serving size, bite size (grams per bite), postmeal satiety, and satiety change were also assessed.In the first study there was a main effect of plate size on grams consumed and number of bites taken such that eating from a large plate led to greater consumption (P=0.001) and a greater number of bites (P=0.001). There was also a main effect of feedback on consumption and number of bites taken such that those who received feedback consumed less (P=0.011) and took fewer bites (P0.001). In the second study there was a main effect of plate size on consumption such that those eating from a large plate consumed more (P=0.003) but did not take more bites. Further analysis revealed a main effect of goal on number of bites taken such that those who received the low goal took fewer bites (P0.001) but did not consume less.Providing feedback on the number of bites taken from a wearable intake monitor can reduce overall intake during a single meal. Regarding the first research question, providing feedback significantly reduced intake in both plate size groups and reduced the overall number of bites taken. Regarding the second research question, participants were successful in eating to their goals. However, individuals in the low goal condition appeared to compensate for the restricted goal by taking larger bites, leading to comparable levels of consumption between the low and high goal groups. Hence, the interaction of technology with goals should be considered when introducing a health intervention.

PurposeAims to show how sensor networks can be used to effectively allow industrial robots to handle unpredicted movements within their workcells.Design/methodology/approachPresents the concept of using sensor networks, using off‐the‐shelf technologies, to sense in real time movements within the workcell, and to feed this information, in real time, to the industrial robot controller, to allow adaptation of the robot to the movements. Results of numerous experiments in intercept and grasp of moving objects using a prototype of the system are reported.FindingsFinds that the prototype system operates effectively and in a robust fashion in over 1,000 trials.Research limitations/implicationsProvides new insight and understanding for engineers working in the area of visually guided robots.Practical implicationsResults are of significant value to practitioners seeking to expand the markets of industrial robots by showing how to expand the capabilities of current systems with off‐the‐shelf technology.Originality/valueIntroduces the concept of using sensor networks to drive the controllers of industrial robots to intercept unpredictable moving objects in their workcells. Results are of value to practitioners working to expand the capabilities of their systems.

Visual sensing for robotics has been around for decades, but our understanding of a timing model remains crude. By timing model, we refer to the delays (processing lag and motion lag) between "reality" (when a part is sensed), through data processing (the processing of image data to determine part position and orientation), through control (the computation and initiation of robot motion), through "arrival" (when the robot reaches the commanded goal). In this study, we introduce a timing model where sensing and control operate asynchronously. We apply this model to a robotic workcell consisting of a Sta/spl uml/ubli RX-130 industrial robot manipulator, a network of six cameras for sensing, and an off-the-shelf Adept MV-19 controller. We present experiments to demonstrate how the model can be applied.

In this paper we present a method to compute the egomotion of a range camera using the space envelope. The space envelope is a geometric model that provides more information than a simple segmentation for correspondences and motion estimation. We describe a novel variation of the maximal matching algorithm that matches surface normals to find correspondences. These correspondences are used to compute rotation and translation estimates of the egomotion. We demonstrate our methods on two image sequences containing 70 images. We also discuss the cases where our methods fail, and additional possible methods for exploiting the space envelope.

We describe an automated method to locate the optic nerve in images of the ocular fundus. Our method uses a novel algorithm we call fuzzy convergence to determine the origination of the blood vessel network. We evaluate our method using 31 images of healthy retinas and 50 images of diseased retinas, containing such diverse symptoms as tortuous vessels, choroidal neovascularization, and hemorrhages that completely obscure the actual nerve. On this difficult data set, our method achieved 89% correct detection. We also compare our method against three simpler methods, demonstrating the performance improvement. All our images and data are freely available for other researchers to use in evaluating related methods.

The construction of a surface model from range data may be undertaken at any point in a continuum of scales that reflects the level of detail of the resulting model. This continuum relates the construction parameters to the scale of the model. We propose methods to dynamically reprocess range data at different scales. The construction result from a single scale is automatically evaluated, causing reconstruction at a different scale when user-defined criteria are not met. We demonstrate our methods in constructing a planar b-rep space envelope (a scene representation) for over 400 range images. The experiments demonstrate the ability to construct 100 percent valid models, with the scale of detail within specified requirements.

A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (1) a common set of 40 laser range finder images and 40 structured light scanner images that have manually specified ground truth and (2) a set of defined performance metrics for instances of correctly segmented, missed, and noise regions, over- and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the specified ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.

A new approach is presented for extracting an explicit 3D shape model from a single range image. One novel aspect is that the model represents both observed object surfaces, and surfaces which bound the volume of occluded space. Another novel aspect is that the approach does not require that the range image segmentation be perfect. The low-level segmentation may be such that the model-building process encounters topology versus geometry conflicts. The model-building process is designed to be "fail soft" in the face of such problems. The portion of the 3D model where a problem presents itself is "glued" together in a manner meant to minimize the disturbance in the 3D shape. The goal is to produce a valid boundary-representation which can be processed by higher-level routines. A third novel aspect of this work is that the implementation has been evaluated on over 200 real range images of polyhedral objects, with no operator intervention and all parameters held constant, and obtained a 97% success rate in creating valid b-reps. >

Measuring the energy intake (kcal) of a person in day-to-day life is difficult. The best laboratory tool achieves 95 % accuracy on average, while tools used in daily living typically achieve 60–80 % accuracy. This paper describes a new method for measuring intake via automated tracking of wrist motion. Our method uses a watch-like device with a micro-electro-mechanical gyroscope to detect and record when an individual has taken a bite of food. Two tests of the accuracy of our device in counting bites found that our method has 94 % sensitivity in a controlled meal setting and 86 % sensitivity in an uncontrolled meal setting, with one false positive per every 5 bites in both settings. Preliminary data from daily living indicates that bites measured by the device are positively related to caloric intake illustrating the potential of the device to monitor energy intake. Future research should seek to further explore the relationship between bites taken and kilocalories consumed to validate the device as an automated measure of energy intake.

This paper concerns a method to automatically detect drusen in a retinal image without human supervision or interaction. We use a multi-level approach, beginning with classification at the pixel level and proceeding to the region level, area level, and then image level. This allows the lowest levels of classification to be tuned to detect even the faintest and most difficult to discern drusen, relying upon the higher levels of classification to use an ever broadening context to refine the segmentation. We test our methods on a set of 119 images containing all types of drusen as well as images containing no drusen or other potentially confusing lesions. Our overall correct detection rate is 87%.