Title:

Estimating Energy Expenditure with Machine Learning - LogSmarter

Humans of all types share a common requirement for energy in the form of calories. This energy depends on a number of factors, including age, height, weight, muscle mass, and daily activity. Total Daily Energy Expenditure (TDEE) is the number of calories an individual burns per day to maintain their weight. According to most research, eating under your TDEE would cause weight loss and eating above TDEE would cause weight gain. For anyone with health, physique or sports performance related goals, knowing your TDEE is essential for optimizing your nutrition. The most common methods of estimating TDEE are through prediction equations. The problem with these equations is that they can be inaccurate by hundreds of calories. These inaccurate calculations cause people to over or underestimate their caloric needs. Therefore, more accurate estimation techniques are needed to account for differences between people. Machine learning is a powerful tool used for data analysis. It is a form of artificial intelligence that can learn from data, identify patterns and make decisions with minimal input from humans. We have developed a machine learning model that yields an accurate and accessible method of TDEE estimation. The results of our research suggest that the error of our model is significantly less than the most popular existing models. Using this model, we have developed an algorithm that provides evidence based recommendations of a caloric intake that aligns with an individual's goals. This algorithm has been incorporated into user friendly software for tracking fitness and nutrition called LogSmarter™. https://logsmarter.net/

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