Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits

• URL: http://arxiv.org/abs/2110.07045v1
• Date: Tue, 5 Oct 2021 10:56:02 GMT
• Title: Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits
• Authors: Mansura A Khan, Khalil Muhammad, Barry Smyth, David Coyle
• Abstract summary: We propose three novel nudging technology, the WHO-BubbleSlider, the FSA-ColorCoading, and the DRCI-MLCP, that encourage users to choose healthier recipes. Results showed that, during the food decision-making process, appropriate healthiness cues make users more likely to click, browse, and choose healthier recipes over less healthy ones.
• Score: 12.07862191291072
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and malnutrition. Over the past decade, artificial-intelligence and medical researchers became more invested in researching tools that can guide and help people make healthy and thoughtful decisions around food and diet. In many typical (Recommender System) RS domains, smart nudges have been proven effective in shaping users' consumption patterns. In recent years, knowledgeable nudging and incentifying choices started getting attention in the food domain as well. To develop smart nudging for promoting healthier food choices, we combined Machine Learning and RS technology with food-healthiness guidelines from recognized health organizations, such as the World Health Organization, Food Standards Agency, and the National Health Service United Kingdom. In this paper, we discuss our research on, persuasive visualization for making users aware of the healthiness of the recommended recipes. Here, we propose three novel nudging technology, the WHO-BubbleSlider, the FSA-ColorCoading, and the DRCI-MLCP, that encourage users to choose healthier recipes. We also propose a Topic Modeling based portion-size recommendation algorithm. To evaluate our proposed smart-nudges, we conducted an online user study with 96 participants and 92250 recipes. Results showed that, during the food decision-making process, appropriate healthiness cues make users more likely to click, browse, and choose healthier recipes over less healthy ones.

Related papers

• NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
  Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
  arXiv  Detail & Related papers  (2024-05-13T14:56:55Z)
• How Much You Ate? Food Portion Estimation on Spoons [63.611551981684244]
  Current image-based food portion estimation algorithms assume that users take images of their meals one or two times. We introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews.
  arXiv  Detail & Related papers  (2024-05-12T00:16:02Z)
• Image-Based Dietary Assessment: A Healthy Eating Plate Estimation System [0.0]
  The nutritional quality of diets has significantly deteriorated over the past two to three decades, a decline often underestimated by the people. This paper introduces an innovative Image-Based Dietary Assessment system aimed at evaluating the healthiness of meals through image analysis. Our system employs advanced image segmentation and classification techniques to analyze food items on a plate, assess their proportions, and calculate meal adherence to Harvard's healthy eating recommendations.
  arXiv  Detail & Related papers  (2024-03-02T21:01:01Z)
• A Food Recommender System in Academic Environments Based on Machine Learning Models [3.42658286826597]
  Machine learning models such as Decision Tree, k-Nearest Neighbors (kNN), AdaBoost, and Bagging were investigated in the field of food recommender systems. The AdaBoost model has the highest performance in terms of accuracy with a rate of 73.70 percent.
  arXiv  Detail & Related papers  (2023-06-26T11:43:37Z)
• NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation [65.47310907481042]
  One in four older adults are malnourished. Machine learning and computer vision show promise of automated nutrition tracking methods of food. NutritionVerse-3D is a large-scale high-resolution dataset of 105 3D food models.
  arXiv  Detail & Related papers  (2023-04-12T05:27:30Z)
• MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model [15.248362664235845]
  A novel restaurant food recommendation system is proposed in this paper. It uses Optical Character Recognition (OCR) technology and a transformer-based deep learning model, Learning to Rank (LTR) model. Our system is able to rank the food dishes in terms of the input search key (e.g., calorie, protein level)
  arXiv  Detail & Related papers  (2022-10-15T11:45:44Z)
• Towards the Creation of a Nutrition and Food Group Based Image Database [58.429385707376554]
  We propose a framework to create a nutrition and food group based image database. We design a protocol for linking food group based food codes in the U.S. Department of Agriculture's (USDA) Food and Nutrient Database for Dietary Studies (FNDDS) Our proposed method is used to build a nutrition and food group based image database including 16,114 food datasets.
  arXiv  Detail & Related papers  (2022-06-05T02:41:44Z)
• Learning Personal Food Preferences via Food Logs Embedding [1.1534313664323634]
  We propose a method for learning food preferences from food logs. Our proposed approach identifies 82% of a user's ten most frequently eaten foods.
  arXiv  Detail & Related papers  (2021-10-29T02:36:24Z)
• Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
  This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
  arXiv  Detail & Related papers  (2021-08-06T05:46:01Z)
• Towards Building a Food Knowledge Graph for Internet of Food [66.57235827087092]
  We review the evolution of food knowledge organization, from food classification to food to food knowledge graphs. Food knowledge graphs play an important role in food search and Question Answering (QA), personalized dietary recommendation, food analysis and visualization. Future directions for food knowledge graphs cover several fields such as multimodal food knowledge graphs and food intelligence.
  arXiv  Detail & Related papers  (2021-07-13T06:26:53Z)
• Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph [16.58534326000209]
  We propose a novel problem formulation for food recommendation, modeling this task as constrained question answering over a large-scale food knowledge base/graph (KBQA) Besides the requirements from the user query, personalized requirements from the user's dietary preferences and health guidelines are handled in a unified way. Our approach significantly outperforms non-personalized counterparts.
  arXiv  Detail & Related papers  (2021-01-05T20:38:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.