International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume:12Issue:05|May2025 www.irjet.net p-ISSN:2395-0072
NutriFit: Smart Nutrition and Personalized Meal Planning System
Rahul Mahale, Manas Vyas, Suchipriya Malge
1Student, Dept of E&TC Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research , Wagholi, Pune, Maharashtra, India
2 Student, Dept of E&TC Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research , Wagholi, Pune, Maharashtra, India
3 Professor, Dept of E&TC Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research , Wagholi, Pune, Maharashtra, India ***
Abstract - This paper presents NutriFit, an intelligent nutritionandmealplanningsystemdesignedtoaddressthe challenges individuals face in creating personalized and balanced meal plans. NutriFit integrates user- specific data such as age, weight, height, activity levels, and dietary preferences to generate personalized meal recommendations. By leveraging machine learning algorithms, the system provides precise recommendations tailored to users' health goals, such as weight loss, weight gain, or maintaining weight. This paper discusses the problemNutriFitaddresses,thearchitectureofthesolution, andfuturepotentialimprovements
Key Words: Nutrition,MealPlanning,Personalized Recommendations,MachineLearning,HealthGoals.
1.INTRODUCTION
In today’s fast-paced world, individuals are increasingly becoming more health-conscious and aware of the significant role that nutrition plays in overall well-being. Poor dietary habits have been linked to a wide range of health issues such as obesity, cardiovascular diseases, diabetes, and other chronic conditions. Despite the plethora of dietary guidelines available, many individuals struggle to maintain a balanced diet that aligns with their specific health needs, lifestyle, and personal preferences. Generic meal plans, while helpful, often fall short in offering the personalization necessary to meet individual goals,preferences,andnutritionalrequirements.
Personalizationiskeywhenitcomestoeffectivenutrition management. Every individual has unique dietary needs based on factors like age, gender, activity level, medical conditions, and body composition. The need for personalized meal planning has grown significantly with increasing awareness about the diversity of nutritional requirements. For instance, an active male athlete would have vastly different dietary needs compared to a sedentary office worker or someone trying to manage diabetes. Therefore, a one-size-fits-all approach is often ineffective, leading to frustration and poor adherence to dietary plans. This is where NutriFit: Smart Nutrition & Personalized Meal Planning System comes into play. NutriFit offers an innovative solution to the challenges of personalized nutrition by
leveraging machine learning techniques and data-driven insights. It utilizes a robust dataset of recipes that contain comprehensive nutritional information such as calories, fat content, carbohydrates, protein, and other essentialnutrients.
By using this data, NutriFit generates personalized meal plans that are tailored to the individual’s specific dietary needs and preferences. The platform’s core functionality revolves around a machine learning model, specifically a nearest neighbors’ algorithm, which enables it to recommend meals based on user inputs like age, weight, height, gender, and activity level. The system can also incorporate specific dietary preferences or restrictions, such as low-carb, vegan, or high-protein diets, ensuring that users receive recommendations that align with their health goals. The backend of NutriFit employs advanced algorithms that process user inputs and provide tailored meal recommendations.The machinelearningmodel uses nutritional values to identify recipes that best match the user’sdietaryrequirements.
In the following sections, this paper will delve into the problem NutriFit aims to solve, the methodology and systemarchitecture used to develop the platform, and the results achieved through this innovative approach to personalizedmealplanning.
2. LITERATUR REVIEW
Managingnutritionandmaintainingabalanceddietcanbe overwhelming and time-consuming, especially when considering individual dietary needs and preferences. Traditional meal planning methods often lack the personalization required to meet specific Health goals, leadingtoinefficienciesanddissatisfaction.Withtheriseof technology particularly Artificial Intelligence (AI), personalized nutrition has become more accessible and precise This paper reviews the integration of AI into meal planning through NutriFit, highlighting its impact customizing nutrition, addressing common dietary challenges, and the potential outcomes of AI-driven recommendations.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056 Volume:12Issue:05|May2025 www.irjet.net p-ISSN:2395-0072
Table1:LiteratureReview
Sr N o. Paper Title Author Publishe r and Publishe d Year limitations
1. Nutritional Diet Recommend er: Evolutionay Approach for Building, Exploring and Recommend ing Complex Items with Application in Nutritional Intervention
2. Human Behaviorbased Personalize d Meal Recommend ation and Menu Planning Social System
3. AI nutrition recommendat ion using a deep generative model and ChatGPT
Bartolo mé OrtizViso Andrea Morale sGarzón MariaJ. MartinBautista MariaAmpar oVila
Tanvir Islam Anika Rahma nJoyita Md. Golam Rabiul Alam, Member Mehedi Hassan, Senior Member ,IEEE
I.P.and D.K. wrote the main man uscr ipt text.
P.D. andK.D. supervi sed the work
IEEE Access Date of publicatio n: 28 June 2023
IEEE Access 12 Aug 2023
1) Difficulty in interpretabilit y. 2) Challenging topresentthe information optimally to users
3) complexityof recommenda tions
1)The approach maynotfully accountfor thediverse rangeof human emotionsand howthey impactfood preferences.
based on their nutritional needs, health goals, and ingredient preferences. The Methodology section outlines the step-by-step process by which NutriFit generates personalized meal recommendations based on user data, healthgoals,andpreferences.NutriFitusesa combination of user input, data processing, machine learning, and feedback loops to continuously optimize meal plans for individual users. The approach involves multiple stages, each contributing to the accuracy and personalization of themealplans.
[1] DataCollectionand Preprocessing: Thefirststepin the system's development involves collecting a comprehensive dataset of food recipes. The dataset includes key nutritional values such as calories, fats, carbohydrates, protein, fiber, and other macronutrientsforeachrecipe,alongwithingredients, preparationtime,andcookinginstructions.
[2] User Input and Personalization: Users are promptedtoprovideessentialpersonaldetailssuchas age, weight, height, gender, activity level, and dietary goals(e.g.,weightloss,weightmaintenance,orweight gain). Additionally, users can input specific dietary preferences, restrictions, or ingredients they wish to includeoravoid.Thisinformationformsthe basisfor thesystem’spersonalizedrecommendations.
[3] Body Mass Index (BMI) and Basal Metabolic Rate (BMR) Calculation: Once the user inputs their data, the system calculates their BMI and BMR using standardformulas:BMIis calculated as weight (kg)/ height^2 (m²), which helps categorize users into different health ranges (e.g., underweight, normal weight,overweight,obesity).
Publisher : Springer Nature Publishe dYear: 2024
1) Diet recommendat ions by ChatGPT can be inaccurate. 2) Recommendati ons have limited understanding ofcontext.
METHODOLOGY & MODELING
The NutriFit: Smart Nutrition & Personalized Meal Planning System is designed to offer personalized nutrition recommendations based on user-specific data, leveragingmachinelearningalgorithmstoprovidehealthy, balanced meal plans. The methodology follows a systematic approach to ensure the system delivers accurate and customized diet recommendations to users
[4] Calorie and Nutritional Needs Estimation: Based ontheuser’sBMR,activitylevel,anddietarygoals,the system estimates daily caloric and nutritional requirements. For example, if a user selects weight loss, the system will adjust their daily caloric intake accordingly, typically by recommending a caloric deficit.
[5] Nearest Neighbors Algorithm for Recommendation: The machine learning component uses the Nearest Neighbors algorithm to generate meal recommendations that closely match the user's nutritionalrequirements.Thealgorithmcomparesthe user'sinputvalues(e.g.,targetcalories,macronutrient distribution) with the nutritional values of various recipesinthedataset.
[6] Recipe Filtering and Customization: Users are given the flexibility to filter recommended recipes based on personal preferencesor dietaryrestrictions (e.g.,vegetarian,gluten-free).Thesystemdynamically adjusts its recommendations based on user input, ensuring that meals not only meet nutritional needs butalsocatertoindividualpreferences
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume:12Issue:05|May2025 www.irjet.net p-ISSN:2395-0072
Flowchart1: Showstheworkingofthesystemin systematicmanner.
Client-Side: The system involves user account creation, data entry, input validation, and desired output selection. Usersinputessentialdetailslikeage,weight,height,gender, and dietary preferences. The system then generates personalized recommendations by analyzing the input and displayingthefinalresults.Theinterfaceshouldbeintuitive and visually appealing, with elements like sliders for weight and dropdown menus for dietary preferences. Feedback mechanisms allow users to provide feedback, enabling the system to refine future suggestions based on usersatisfaction.
Server-Side: The system uses an API key for secure communication and data retrieval, ensuring user information is protected. The server retrieves user data, including historical preferences and dietary restrictions. The K-Nearest Neighbors (KNN) algorithm is used to identify similar users or meal patterns. The system generates personalized food recommendations based on similarity analysis, recommending meals thathaveproven successful for similar users. The server then packages the recommendationsandsendsthembacktotheclientviaan API response. The server logs user interactions and feedback to refine the algorithm and improve future recommendations.
Algorithms –Nearest Neighbors Algorithm: The Nearest Neighbors Algorithm (NNA) is central to NutriFit’s recommendation system. NNA operates by finding the closest data points to a given input based on a distance metric.FortheNutriFitsystem,thisalgorithmusescosine similarityasthe metricto measuretheclosenessbetween
a user’s desired nutritional values and the recipes available in the dataset. By continuously evolving and incorporating the latest advancements in AI, nutrition science, and health technology, NutriFit has the potential to become a comprehensive, global solution for personalizedhealthandnutrition.
1) Data Preprocessing: All nutritional values (e.g., calories, fat, protein) are first standardized using Z-score normalization to ensure that all features are on the same scale. This is necessary because thedifferentnutritionalfeaturesmayhavevarying ranges, and without normalization, the feature with the largest range may dominate the distance calculation.
2) Cosine Similarity Calculation: After standardization,thecosinesimilaritybetweenthe input nutritional values and each recipe in the dataset is calculated. The cosine similarity betweentwovectors
a. \(A\)and\(B\)isgivenby:
b. \ [\text {CosineSimilarity} (A,B) = \frac {A\cotB}
c. {||A||\,||B||}\]
d. where\(A\cotB\)isthedotproductof the vectors, and \(||A|| \) and \( ||B|| \) arethemagnitudesofthevectors.
3) Nearest Neighbour Searching: Once the similarities are computed, the top \ (k \) nearest recipes are selected based on the highest similarity scores. The value of \ (k \) is a configurable parameter that allows for flexibility in the number of recommendations. For NutriFit, \( k = 5 \) is used by default to provide five personalizedmealsuggestions.
4) Result Output: The algorithm returns the top \ (k \) recipes that best match the user's nutritional preferences.Therecipesarerankedindescending orderofsimilarity.
The Nearest Neighbors Algorithm ensures that NutriFit users receive meal recommendations that closely align with their personalized nutritional requirements, making itanefficienttoolfortailoredmealplanning.InNutriFit,the KNN algorithm is used to recommend meals that are aligned with the user’s personal preferences and health goalsby finding similar user profiles or meal options. The future of NutriFit is bright, with opportunities for technological innovation, market expansion, and deeper integration with healthcare and fitness ecosystems. By continuously evolving and incorporating the latest advancements in AI, nutrition science, and health technology, NutriFit has the potential to become a comprehensive, global solution for personalized health andnutrition.
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4. RESULTS
NutriFit provides users with personalized nutrition recommendations based on their dietary preferences, health goals, and nutritional requirements. By analyzing various nutritional parameters, users can receive tailored meal plans and recipe suggestions that align with their health objectives. Additionally, the KNN algorithm proved to be a strong foundation for generating relevant and accuratemealplansbasedonuserinput.
In a research paper, the Results section is crucial as it presents the outcomes of the experiments, analyses, and tests conducted as part of the project. For the NutriFit: SmartNutrition&PersonalizedMealPlanning System,the results focus on evaluating the effectiveness of thesystem in generating personalized meal recommendations, improving user health outcomes, and the overall performance of the algorithm. Below is a detailed breakdownofthetypesof resultsthatcan be expected for NutriFit.Withscalableinfrastructureandaresponsiveuser interface, NutriFit shows promise in supporting a larger user base while maintaining a high level of service and personalization The implementation of NutriFit has demonstrated significant improvements in personalized dietaryrecommendationsanduserengagement,leadingto betteradherencetomealplansandmoreconsistenthealth outcomes. Below, we outline the key results observed duringtheinitialtestinganddeploymentphases:
Fig2:UserDatacollection&ValidationStage:forBMI & personalizeddietrecommendations(Thisstageiscrucial for generatingaccurateandpersonalizeddiet recommendations,ensuringinputdataisvalid,relevant, andusable.)
Fig.3: BMIandCaloriesCalculator:HeretheBodyMass Index anddailycalorieintakeiscalculated
Fig4: GeneratedDietRecommendation:Recommends alistof Recipes(StoredinourDataset)totheUser.
Fig.5: ResultOutput:Thestudycomparesthenutritional breakdownandcaloriccontentofuser-selectedtotal calories.
Fig.6: SelectInputsforCustomFoodRecommendations (Here theuserselectsitsdietarypreferences, suitable/recommended.)
Fig1: NutriFitUserInterface
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Fig.7: PersonalizedRecipeaccordingtouserpreferences. (Personalizedrecipesuggestionsbasedonuser'sdietary information,ensuringaseamlessmeal-planning experience.)
Fig.8: OverviewofNutritionalValuesofgeneratedrecipes. (NutriFit'snutritionaloverviewsectionoffersdetailed nutritional valuesforrecipes,aligningwithdietarygoals likeweightlossand musclegain,ensuringdailycaloricand macronutrientintake.)
5. DISCUSSION / ANALYSIS OF RESULT
1. NutriFitUserInterfaceOverview:TheNutriFitsite's main view is user-friendly, allowing easy entry of personal data and access to meal ideas. The interface is streamlined for easy navigation, enhancing user engagement. Clear input fields ensure accurate data input for well-defined recommendations.However,real-timetipsorhints forfirst-timeuserscouldimprovedataaccuracy.
2. User Data Collection & Validation Stage: NutriFit collects and validates user information like weight, height, age, and diet type to ensure consistency, minimize errors, and make meaningful BMI calculations,incorporatingfitnesstrackerinput.
3. BMIandCaloriesCalculator:Theillustrationshowsa user-inputBMIcalculation,whichhelpssetdietary targets for calorie intake, with a more accurate measure of BMI when incorporating fat percentage.
4. Generated Diet Recommendation Output: The system uses K-Nearest Neighbors to provide personalized meal plans, adapting to user needs
over time. Improvements include integrating feedback,displayingpreparationtimeandcost,and improvinguserconvenience.
5. Comparison of User-Selected Calories and Weight Loss Target: A comparison chart visualizes user calorieintakeforweightloss,aidinginmonitoring adherence to plans. Trend lines over time could enhanceprogresstracking.
6. Custom Inputs for Food Recommendations: This section explains how users can filter inputs like vegetarian and low-carb to receive personalized food suggestions, while also allowing for data expansionbeyondFood.com.
7. Nutritional Overview of Generated Recipes: The picture provides detailed nutritional breakdowns for suggested recipes, aiding users in making informed diet choices and comparing them with dailynutritionalneeds.
6. FUTURE IMPLEMENTATION:
1. Integration with Wearable Devices: NutriFit can integrate with wearable devices to provide real-time data on users' physical activity and health metrics, enabling personalized meal recommendations based on actual exertion, calorie expenditure, and overall health status. Integrating wearable devices into the NutriFit platform not only enhances the personalization of nutrition plans but also empowers users with real-time feedback and insights. This integration ultimately leads to more effective health management, improved user engagement, and better outcomesinachievingfitnessandnutritionalgoals.
2. Enhanced Meal Diversity: We aim to expand the NutriFit system to include a wider range of culturally diverse recipes. This will ensure that users from different regions can receive meal recommendations thatalignwiththeirpersonalandculturalpreferences.
3. Real-Time Nutritional Tracking: We plan to integrate NutriFit with real-time fitness tracking devices and apps. This feature will dynamically adjust meal recommendations based on the user’s physical activity, daily caloric burn, and real-time health metrics, creating a more tailored experience. NutriFit integrates real-time nutritional tracking with fitness devices, enhancing meal recommendations and user engagement. This dynamic approach empowers
informed dietary choices, aligning with fitness goals andpromotinghealthierlifestyles.
4. Customizable Dietary Plans: The future implementation will allow users to create their own custommealplans basedonspecifichealthgoalssuch as muscle gain, weight maintenance, or managing medical conditionslike diabetesorheartdisease.This
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willaddahigherlevelofpersonalizationtothesystem. NutriFitwilloffercustomizabledietaryplans,catering toindividualhealthgoalsandmedicalconditions.This feature enhances user engagement, satisfaction, and promotes better health outcomes by aligning meal planswithspecificneedsandpreferences.
5. Expansion of the Dataset and Recipe Sources: Currently,NutriFitreliesontheFood.comdataset,which contains over 500,000 recipes. However, the system could further improve by integrating additional datasets from another recipe sources, such as Allrecipes,Epicurious,andBBCGoodFood.
NutriFit's platform will be enhanced by integrating recipesourceslikeAllrecipes,Epicurious,andBBCGood Food, providing a diverse range of recipes, improved nutritional insights, and customizable search features, fostering a more engaging and health-conscious user community.
6. Multilingual and Cross-Cultural Adaptation: NutriFit plans to expand globally by adapting to diverse languages and diet preferences. This will enable the company to cater to diverse cultures through its meal plans, considering local cuisines, traditions, ingredients, regional dietary needs, and foodavailability.
7. CONCLUSION
NutriFit: Smart Nutrition & Personalized Meal Planning System provides an innovative solution to personalized diet recommendations using machine learning. By analyzinguser-specificdatasuchasheight,weight, activity levels, and dietary preferences, NutriFit generates customized meal plans that align with individual health goals,whether it isweightloss,maintenance, or gain. This system not only simplifies meal planning but also promotes healthier eating habits by offering nutritionally balancedoptions.Theimplementationofmachinelearning algorithms for recipe recommendation, along with the integration of nutritional analysis, sets NutriFit apart as a tool that can continuously adapt to users' needs. The potential future enhancements, such as real-time tracking, multilingual support, and integration with grocery services, will further enrich the user experience and make healthyeatingmoreaccessibleandconvenient.Whilethere are still areas for improvement, NutriFit has laid the foundation for a comprehensive, personalized nutrition system thatcancontributetohealthierlifestyles and wellbeing.
NutriFit is a personalized nutrition service platform that uses artificial intelligence and machine learning to derive tailored meal plans based on an individual user's preference, health data, and goals. The system provides recommendationsthatgrowwithusersastheyputinmore data and feedback-all aimed at ensuring nutritionally balanced meal plans in recognition of their dietary preferencesandrestrictionsaswellashealthobjectives.
8. REFERENCES
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[2] Human Behaviour-basedPersonalized Meal Recommendation and Menu Planning Social System:TanvirIslam,AnikaRahmanJoyita,Md.Golam Rabiul Alam, Member, IEEE, Mohammad Mehedi Hassan, Senior Member, IEEE, Md. Rafiul Hassan Senior Member, IEEE, and Raffaele Gravina:(PDF) Human Behavior-based Personalized Meal RecommendationandMenuPlanningSocialSystem
[3] Evolutionary Approach for Building, Exploring and Recommending Complex Items With Application in Nutritional Interventions: Bartolome Ortiz-Viso, Andrea Morales-Garzon, Maria J. Martin-Bautista, Maria- Amparo Vila: IEEE Access Date of publication: 28June2023:IEEEXploreFull-TextPDF:
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