This article considers detecting eating in free-living humans by tracking wrist motion. We are specifically interested in the effect of secondary activities that people conduct while simultaneously eating, such as walking, watching television, or working. These secondary activities cause wrist motions that obfuscate those associated with eating, increasing the difficulty of detecting periods of eating. We collected a large dataset of 4,680 hours of wrist motion from 351 participants during free living. Participants reported secondary activities in 72% of meals. Analysis of wrist motion data revealed that the wrist was resting 12.8% of the time during self-reported meals compared to only 6.8% of the time in a cafeteria dataset, whereas walking motion was found 5.5% of the time during meals in free living compared to 0% in a cafeteria. Augmenting an eating detection classifier to include walking and resting detection improved accuracy from 74% to 77% on our free-living dataset ( t [353] = 7.86, p < 0.001). Although eating detection could be improved using more sophisticated machine learning methods or sensor modalities, all approaches would be affected by secondary activities, as they affect the labeling of data itself. Our work suggests that future work should collect detailed ground truth on secondary activities being conducted during eating, as these activities could hold insights into when an eating activity starts or stops in the absence of video-based ground truth.

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Background New technologies are emerging that may be able to help individuals engage in healthier eating behaviors. One paradigm to test the efficacy of a technology is to determine its effect relative to environmental cues that are known to cause individuals to overeat. Objective The purpose of this work was to independently investigate two questions: 1) How does the presence of a technology that provides bite count feedback alter eating behavior?; and 2) How does the presence of a technology that provides bite count feedback paired with a goal alter eating behavior? Design 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. Participants/setting In the first study, 94 subjects (62 female, Age 19.0±1.6 years, BMI 23.04±3.6) consumed lunch in a laboratory. The second study examined 99 subjects (56 female, Age 18.5±1.5 years, BMI 22.73±2.70) under the same conditions. Intervention 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). Statistical Analyses Effect of PLATE SIZE, FEEDBACK, and GOAL on consumption (grams) and number of bites taken were assessed using 2×2 ANOVAs. As adjunct measures, the effects of serving size, bite size (grams per bite), post-meal satiety and satiety change were also assessed. Results 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=.001) and a greater number of bites (p=.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=.011) and took fewer bites (p<.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=.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 (p<.001) but did not consume less. Conclusion 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 restric...

Unmanned systems will play an increased role in the future beyond military application including but not limited to: search and rescue, border patrol, homeland security, and natural disaster relief operations. Current models of unmanned system operations, such as those used for unmanned aerial vehicles, require multiple operators to control a single vehicle. This multioperator-single vehicle ratio will soon shift to a multioperator-multivehicle model as the number of unmanned systems increase and work in unison to complete a mission. The purpose of this study was to determine the utility of a physiological measure i.e. heart rate variability (HRV), to assess operator workload in a single operator-multivehicle command and control simulation. An internally developed command and control simulator is described and observed effects of mental workload on HRV are reported. Results suggest that HRV can be used to assess operator workload during a command and control simulation of multiple unmanned aerial vehicles.

According to a recent National Health and Nutrition Examination Survey, overweight and obesity have reached epidemic levels in the United States. Researchers are increasingly engaged in exploring eating behavior with the goals of trying to understand what elements of eating behavior might lead to overweight and obesity and applying knowledge from these studies to encourage people to engage in healthy eating behaviors. The purpose of the current study was to determine the utility of a new laboratory eating paradigm that attempts to create a natural social eating environment while maintaining the control possible within a university laboratory. Known effects of gender on eating behavior (e.g. consumption rates and bite size), positive subjective ratings of the food item used, and subjective ratings of perceived eating behavior were replicated to show the utility of the paradigm.