Accurate Calorie Research Articles

A Multi-Model Machine Learning Approach for Monitoring Calories Being Burnt During Workouts Using Smart Calorie Tracer

INTRODUCTION: In today's health-conscious world, accurate calorie monitoring during exercise is crucial for achieving fitness goals and maintaining a healthy lifestyle. However, existing methods often lack precision, driving the need for more reliable tracking systems. This paper explores the use of a multi-model machine learning approach to predict calorie burn during workouts by utilizing a comprehensive dataset.
 OBJECTIVES: The objective of this paper is to develop a user-friendly program capable of accurately predicting calorie expenditure during exercise, leveraging advanced machine learning techniques.
 METHODS: Techniques from social network analysis were employed to analyze the dataset, which included information on age, gender, height, weight, workout intensity, and duration. Data preprocessing involved handling missing values, eliminating irrelevant columns, and preparing features for analysis. The dataset was then divided into training and testing sets for model development and evaluation. Machine learning models, including Neural Networks, AdaBoost, Random Forest, and Gradient Boosting, were chosen based on their performance in regression tasks.
 RESULTS: The neural network model demonstrated superior performance in predicting calorie burn, outperforming other models in terms of MSE, RMSE, and an R2 score. Data visualization techniques aided in understanding the relationship between variables and calorie burn, highlighting the effectiveness of the neural network model.
 CONCLUSION: The findings suggest that a multi-model machine learning approach offers a promising solution for accurate calorie tracking during exercise. The neural network model, in particular, shows potential for developing user-friendly calorie monitoring applications. While limitations exist, such as dataset scope and environmental factors, this study lays the groundwork for future advancements in calorie monitoring and contributes to the development of holistic fitness applications.

JoulesEye

Smartphones and smartwatches have contributed significantly to fitness monitoring by providing real-time statistics, thanks to accurate tracking of physiological indices such as heart rate. However, the estimation of calories burned during exercise is inaccurate and cannot be used for medical diagnosis. In this work, we present JoulesEye, a smartphone thermal camera-based system that can accurately estimate calorie burn by monitoring respiration rate. We evaluated JoulesEye on 54 participants who performed high intensity cycling and running. The mean absolute percentage error (MAPE) of JoulesEye was 5.8%, which is significantly better than the MAPE of 37.6% observed with commercial smartwatch-based methods that only use heart rate. Finally, we show that an ultra-low-resolution thermal camera that is small enough to fit inside a watch or other wearables is sufficient for accurate calorie burn estimation. These results suggest that JoulesEye is a promising new method for accurate and reliable calorie burn estimation.

Fast and Accurate Calorie Count Prediction from Food Images using a Convolution Neural Network Method

In the past ten years, with advances in deep learning techniques, automated object recognition has come very near to human levels of accuracy. Fast, automatic, and consistent image-based food calorie calculation is becoming a requirement with the increasing growth of overweight and other lifestyle- related disorders throughout the globe. Accurate and insightful solutions may be provided in the form of a mobile app with the aid of a deep learning-based automatic object recognition system. However, real-time image processing necessitates a high level of processing speed, which is a major consideration for such applications. Although several researchers have looked at estimating calories based on pictures of food, there is currently no image-driven, lightweight, rapid, and accurate food calorie estimation method. In this study, we offer a technique for recognizing common meals captured with a mobile phone camera by using Convolution Neural Networks (CNN) with optimum parameters. Calories and other nutritional information may be deduced from the known food class after the food items have been identified. Our research shows that our suggested method not only guarantees precision but also has the potential to greatly streamline the complex, time-consuming, and labor-intensive processes now used for estimating calorie intake by turning them together into real-time automation techniques.

Accurate Calorie Burn Prediction with Machine Learning: XGBoost in Focus

Accurate Calorie Burn Prediction with Machine Learning: XGBoost in Focus

NIRSCam: A Mobile Near-Infrared Sensing System for Food Calorie Estimation

The calculation of calorie consumption is of paramount importance for the human diet and health management. Most existing solutions use image-processing techniques to identify the food type and refer to the nutrition table to compute the total calorie, which is quite challenging to differentiate foods that look the same but contain vastly different quantities of calories. To address this issue, we propose to leverage near-infrared spectroscopy (NIRS) to derive the concentration of nutrients based on the unique absorption spectrum of foods. Instead of using the professional NIRS system that is bulky, expensive, and impractical for daily use by nonexpert users, we develop a low-cost portable NIRS system using commercial LEDs. As the quality of the signals of the low-power LEDs is relatively poor, we carefully design modulation schemes and interference elimination algorithms to improve the signal quality and remove the interference. Extensive experiments show that NIRSCAM outperforms the image-based baseline in achieving more accurate calorie estimation, especially for look-alike foods and is robust to various environmental factors.

The impact of menu size on calorie estimation

Consumers seeking to maintain a healthy diet commonly estimate their daily caloric intake. Despite the importance of accurate calorie estimation, contextual and situational factors significantly influence consumers’ calorie estimations. The current research investigates how the number of items listed on a menu systematically influences consumers’ calorie estimations. Four experiments show that consumers estimate an item’s caloric content as greater (less) in a menu listing more (fewer) items. The results indicate that consumers are more likely to form a wider (vs. narrower) range of calorie estimates for a larger (vs. smaller) menu, leading them to estimate a target item’s caloric content as greater in a larger menu. We suggest that the number of menu items can prime a sense of magnitude, which in turn affects one’s calorie estimation process. • The paper investigates how the number of items listed on a menu influences consumers’ calorie estimations. • Consumers estimate an item’s caloric content as greater (less) in a menu listing more (fewer) items. • Consumers form a wider range of calorie estimates for a larger menu, leading them to higher caloric content of a target item. • The number of menu items primes a sense of magnitude, which in turn affects one’s calorie estimation process.

Food Sustenance Estimation Using Food Image

The upcoming generation is at high risk of developing many health issues like heart diseases, metabolic diseases and other life-threatening problems with high mortality as a consequence of obesity due to intake of unhealthy food which is totally deviated from a normal balanced diet with appropriate calories, proteins, vitamins and carbohydrates. In this work, the nutrient intake is calculated using food image. Our system provides efficient segmentation algorithms for separating food items from the plate. The given 2D image of food is converted into 3D image by generating its depth map for volume generation and color, texture and shape features are extracted. These features are fed as input into multi-class support vector machine classifier for learning. The learning phase involves training of various mixed and non mixed food items. The testing phase includes query image segmentation and classification for identifying the type of food and then finding calories using the nutrition data table. We have also estimated the ingredient and decay of food items. Our result shows accurate calorie estimation for various kinds of food items.

Nutritional Status of Reproductive Ethnic Woman Living in Chittagong Hill Tracts of Bangladesh: Influence of Socio-economic and Nutritional Factors

Aim This cross-sectional study assessed the nutritional status of ethnic reproductive women aged between 15-40 years, comprising 85 lactating women (LW) and 86 non-pregnant-non-lactating women (NPNLW) who lived in various parts of three hilly districts (Bandarban, Khagrachari and Rangamati) of the Chittagong Hill Tracts (CHTs) of Bangladesh. Materials and Method Research tool was pretested semi-structured questionnaire for this observational study. Anthropometric indices like weight, height and mid-upper-arm-circumference (MUAC) were used in assessing the nutritional status. Results Most (44.4%) of the ethnic women were Marma, followed by Chakma (39.8%), Bam (8.2%) and Tripura (5.8%). No significant (P>0.05) differences were observed in socio-economic variables between LW and NPNLW. Overall, mean age of the women was 28 (±7.2) years; mean family income was 18,822.2 (±3091) BDT and majority (86%-87%) were reported to be food secured throughout the year. Mean Dietary calorie (2631±798 kcal) intake of both groups were up to the mark according to the recommended dietary allowances (RDAs) and most (80.3%-86%) of them also possessed good nutritional knowledge. However, no significant (P>0.05) differences were observed in dietary calorie intake and nutritional knowledge score between two groups of women. Overall, most (68.4%) of the women had normal body max index (BMI kg/m2), 11.7% were found to be undernourished or thin and 19.9% were overweight. It was noticed that better nutritional status of LW and NPNLW were significantly associated with literacy (P<0.05), occupancy in household chores (P<0.05), better income (P<0.05), food security (P<0.05), recommended calorie intake level (P<0.05) and better nutritional knowledge score (P<0.05). Conclusion This study showed that vast majority of the ethnic reproductive women of CHTs are in better nutritional status with normal BMI (kg/m2) and daily calorie intake according to RDA. Most of them were food secured and their nutritional knowledge score was also up to the mark. Better socio-economic condition, nutritional knowledge and accurate calorie intake play a vital role in achieving healthy nutritional status of the two women groups of CHTs.

The Impact of Caloric Information on College Student's Fast Food Purchasing Intentions

Background: Fast food establishments are available on many college campuses and, as a result, many students consume foods that are high in calories and contribute to unhealthy weight gain. Purpose: This study measured college students' knowledge of the calorie content for fast food items and whether the provision of calorie information for those foods influenced their future purchasing intentions. Method: Randomly selected undergraduate college students (N = 201) completed an online survey that measured baseline knowledge of calorie content for a fast food item and intention to purchase that item in the future. After provision of accurate calorie information, students were posttested for intention to purchase that item in the future. Results: The majority of students underestimated calorie content for fast food items. After receiving accurate calorie information, those who initially underestimated calorie content were significantly more likely to change their intention to purchase that food item in the future. Discussion: Many college students are interested in avoiding high-calorie fast food items but are uninformed about calorie content. Translation to Health Education Practice: Colleges should provide calorie information for fast food items at the point of purchase so that students can make informed decisions that will promote their health.

The Energy Content of Restaurant Foods Without Stated Calorie Information

National recommendations for the prevention and treatment of obesity emphasize reducing energy intake through self-monitoring food consumption. However, little information is available on the energy content of foods offered by nonchain restaurants, which account for approximately 50% of restaurant locations in the United States. To measure the energy content of foods from independent and small-chain restaurants that do not provide stated information on energy content. We used bomb calorimetry to determine the dietary energy content of the 42 most frequently purchased meals from the 9 most common restaurant categories. Independent and small-chain restaurants were randomly selected, and 157 individual meals were analyzed. Area within 15 miles of downtown Boston. A random sample of independent and small-chain restaurants. Dietary energy. All meal categories provided excessive dietary energy. The mean energy content of individual meals was 1327 (95% CI, 1248-1406) kcal, equivalent to 66% of typical daily energy requirements. We found a significant effect of food category on meal energy (P ≤ .05), and 7.6% of meals provided more than 100% of typical daily energy requirements. Within-meal variability was large (average SD, 271 kcal), and we found no significant effect of restaurant establishment or size. In addition, meal energy content averaged 49% greater than those of popular meals from the largest national chain restaurants (P < .001) and in subset analyses contained 19% more energy than national food database information for directly equivalent items (P < .001). National chain restaurants have been criticized for offering meals with excess dietary energy. This study finds that independent and small-chain restaurants, which provide no nutrition information, also provide excessive dietary energy in amounts apparently greater than popular meals from chain restaurants or information in national food databases. A national requirement for accurate calorie labeling in all restaurants may discourage menus offering unhealthy portions and would allow consumers to make informed choices about ordering meals that promote weight gain and obesity.

Quantifying Accurate Calorie Estimation Using the “Think Aloud” Method

Clients often have limited time in a nutrition education setting. An improved understanding of the strategies used to accurately estimate calories may help to identify areas of focused instruction to improve nutrition knowledge. A "Think Aloud" exercise was recorded during the estimation of calories in a standard dinner meal (415 kcal) in 15 nutrition-trained and 15 untrained subjects. Accuracy of participant estimation was assessed, and estimation strategies were compared in accurate vs inaccurate estimators. Accurate participants were more likely to demonstrate an ability to convert between common portion size measurements and serving sizes, possess some knowledge of energy density, and properly implement basic math skills. The "Think Aloud" methodology was informative in assessing the cognitive processes behind a nutrition exercise, and further use is warranted. Focused instruction in portion size conversion, energy density, and the use of math skills may enhance calorie estimation accuracy.

Predicting Energy Expenditure From Heart Rate During Exercise: What Variables Are Most Important?

Assessing the energy expenditure (EE) of physical activity is of interest to researchers, fitness/health professionals, and individuals engaging in physical activity. Accordingly, there have been attempts to develop accurate calorie prediction equations based on exercise heart rate (HR) using an equation that takes into account exercise HR and other variables (i.e., body weight, age, sex). Unfortunately, they do not factor in other key variables such as aerobic capacity, resting metabolic rate (RMR), and percentage body fat (BF). PURPOSE: To provide a regression equation to predict EE from HR during aerobic exercise. METHODS: The participants were 65 apparently healthy adults (22 males;43 females; age: 28.1±7.4 yr, BMI: 23.6±3.7 (kg*m-2). Participants engaged in 4-min periods of treadmill exercise at five different randomized workloads (0, 2.0, 3.5, 4.5, and 6.0 mph) in which HR and EE were continuously recorded via an EKG and indirect calorimetry (IC). RMR was assessed via IC during 20 minutes of supine rest, VO2max was obtained via a graded exercise test, and BF was obtained via hydrostatic weighing. Data from the last 2 minutes of each period were grouped into 3 categories based on the percentage of heart rate reserve (HRR; HRmax-HRrest) as follows: L=0-19.9%, M=20-59.9%. and H=60+%. Stepwise linear regression was used to create models for predicting EE from %HRR and other measured predictors for each intensity category. RESULTS: Three models were created for: males,females, and both sexes (Table 1). The variables that were consistent significant predictors of EE were %HRR, RMR, and age. CONCLUSION: Depending on intensity, prediction of EE is most accurate when %HRR, RMR, and age are used in the prediction equation.Table 1