Smartphone-based Calorie Estimation From Food Image Using Distance Information

Personal assistive systems for diet control can play a vital role to combat obesity. As smartphones have become inseparable companions for a large number of people around the world, designing smartphone-based system is perhaps the best choice at the moment. Using this system people can take an image of their food right before eating, know the calorie content based on the food items on the plate. In this paper, we propose a simple method that ensures both user flexibility and high accuracy at the same time. The proposed system employs capturing food images with a fixed posture and estimating the volume of the food using simple geometry. The real world experiments on different food items chosen arbitrarily show that the proposed system can work well for both regular and liquid food items

Mindful Eating: Trait and State Mindfulness Predict Healthier Eating Behavior

Obesity and excess weight are significant societal problems. Mindfulness may encourage healthier weight and eating habits. Across four studies, we found a positive relation between mindfulness and healthier eating. Trait mindfulness was associated with less impulsive eating, reduced calorie consumption, and healthier snack choices. In addition, we found a causal effect of mindfulness on healthier eating. An experimental manipulation of state mindfulness led participants to consume fewer calories in a spontaneous eating task. We also found preliminary evidence that mindfulness affects eating behavior by encouraging attitudinal preferences for healthier foods. Taken together, these results provide strong evidence that mindfulness encourages healthier eating, even in the absence of specific instruction in mindful eating. These results suggest that generic mindfulness-based strategies could have ancillary benefits for encouraging healthier eating behavior

The Effects of Relative Food Prices on Obesity – Evidence from China: 1991-2006

This paper explores the effects of relative food prices on body weight and body fat over time in China. We study a cohort of 15,000 adults from over 200 communities in China, using the longitudinal China Health and Nutrition Survey (1991-2006). While we find that decreases in the price of energy-dense foods have consistently led to elevated body fat, this price effect does not always hold for body weight. These findings suggest that changes in food consumption patterns induced by varying food prices can increase percentage body fat to risky levels even without substantial weight gain. In addition, food prices and subsidies could be used to encourage healthier food consumption patterns and to curb obesity.Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Health Economics and Policy,

A Picture of Hartford\u27s Community Food Environment: An Image Recognition Approach

The rise in recent years of research dedicated to community food environments has produced valuable insights but has focused primarily on one dimension of access to healthy food: availability. This study expands the current research and utilizes an innovative approach in generating a food environment index by focusing on consumer choice in restaurants. Using food images crowdsourced from Google Place (n=19,907) and TripAdvisor (n=3,252) in restaurants (n=487) of the Greater Hartford Area, we employed a deep-learning based food-image-recognition technique to identify the food type and nutrition information from these food images, which were also validated by manual coding. We then generated a community food environment index by aggregating the deep-learned nutrition information from each restaurant on the census-tract level and explored this index’s relationships with each neighborhood’s socio-demographic characteristics and two established food environment indices, namely the USDA’s Food Access measure and the mRFEI. Our results showed that deep learning results were reasonably accurate (75% accuracy when compared with manual coding), and the resulting food environment index was significantly correlated with the share of single parent households (p\u3c0.05) and people living in group quarters (p\u3c0.01) in each census tract. We also observed moderate consistency and weak correlations between our food environment index and both established indices. This pilot study shows that a deep-learning based food-image-recognition approach has the potential to map out local food environment and complement other food environment indices by accounting for food environment-diet relationship and portraying the individual’s choices in built food environments

IoT Based Computer Vision System for Nutrition Management

As people around the globe are becoming conscious about their weight, consume healthy and low calorie food and keep away from obesity, it's an urge to establish a reliable system with high accuracy and efficiency for calorie and nutrition measurement in fruit/vegetable. The proposed model is developed to assist patients and dieticians to compute daily intake of calories. In this approach, 5 different machine learning models are used to predict classification accuracy. Our system includes camera and intelligent mat to capture the picture of the fruit/vegetable, in order to calculate the consumption of calorie. The proposed model achieves 88% accuracy with different testing-training cross validation dataset