Personalized Calories Burned Prediction for Human Support Fitness Using Neural Network Models

Accurate prediction of calorie expenditure is essential for personalized fitness management yet conventional estimation methods based on Metabolic Equivalent of Task (MET) values fail to account for individual physiological differences. This study developed and compared three neural network models: Artificial Neural Network (ANN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN) for predicting calories burned during aerobic exercise using Calories.csv and exercise.csv datasets sourced from Kaggle. The dataset was preprocessed through categorical encoding and feature engineering to expand the feature set. Exploratory data analysis was performed ant it identified exercise duration, heart rate, and body temperature as the strongest predictors of calorie expenditure. All three models were trained using back propagation and the Adam optimizer, and evaluated using R 2 , RMSE, MAE, and MAPE among other metrics. All models achieved over 99% explained variance, with the DNN emerging as the best performer (R 2 = 0.9998), followed by the ANN (R 2 = 0.9997) and the RNN (R 2 = 0.9975). The RNN's comparatively weaker performance could be attributed to the non-sequential nature of the dataset. The DNN was subsequently deployed as a Flask-based web application providing real-time calorie predictions alongside health metrics such as BMI and exercise intensity. The study demonstrates that deep learning, particularly DNN architectures, offers a highly accurate and practical solution for personalized calorie prediction in fitness and health applications.