Physical Fitness Test Data Analysis and Training Program Recommendation Based on Machine Learning

Abstract

Physical fitness is the state of being physically healthy and capable of performing daily tasks with vigor and resilience. Physical fitness and machine learning intersect in various ways, primarily through the use of wearable devices, fitness apps, and data analysis. Wearable fitness trackers equipped with sensors, such as heart rate monitors, accelerometers, and GPS trackers, collect vast amounts of data on individuals' physical activity, sleep patterns, and vital signs. The paper presents an innovative approach to physical fitness assessment and training program recommendation using the Gradient Probabilistic Automated Recommender System with Machine Learning (GPA-RS-ML). This system utilizes machine learning techniques to assess fitness data from individuals and then suggests training programs that are customized to their specific goals and needs. By incorporating gradient values and probabilistic predictions, the GPA-RS-ML algorithm offers a comprehensive and individualized approach to fitness training, enhancing the efficiency and effectiveness of training interventions. The study demonstrates the efficacy of the GPA-RS-ML system in accurately predicting suitable training programs for participants, considering their unique fitness profiles and preferences. This research contributes to the advancement of automated fitness assessment and recommendation systems, providing a valuable tool for fitness professionals and enthusiasts to optimize fitness outcomes and improve adherence to training regimens.