Machine Learning-Based Calorie Burn Estimation for Enhanced Physical Activity Monitoring

The rapid pace of technological advancements has created a multitude of prospects and challenges, especially in regard to mental health and personal fitness. More often than not, the fast pace of modern living forces people to make unhealthy food choices that are more readily available, leading to increased caloric intake, which represents one of the primary causes of the growing rates of obesity. This situation strongly emphasizes the urgent need for effective control over calorie intake and expenditure along with other resources to promote healthier lifestyle habits. The study takes a comprehensive approach to these issues by propounding intricate methods in machine learning designed to calculate caloric expenditure incurred during various phases of physical activity. It primarily helps people by providing information to maintain consistency in their exercise routines and promote healthy eating habits. The model proposed in this study combined complex algorithms like XGBoost, Decision Trees, and Support Vector Regression (SVR) for a highly massive dataset of over 15,000 data points. Interestingly, XGBoost reflected an observed mean absolute error of as low as 1.48, thus improving the accuracy of calorie expenditure estimation and encouraging users to engage in regular physical activity. This approach is much more impactful than just analyzing caloric intake. The Machine Learning algorithm, as it learns and evolves from experience with its user, can output thematic insights and recommendations that nudge individuals toward active fitness and nutritional oversight management. The findings highlight the impact of technology on constructive behavioral modification and the potential for improving lives as individuals transition to healthier lifestyles.