As the volume of basketball training and game data grows rapidly, the current approaches to estimating the energy consumption of sports and various health-related issues include low precision of the round level energy consumption, lack of individual differences, and the inability to respond to the real-time risk of training in case of danger. To overcome this, this paper presents a big data-based algorithm that determines the energy consumption of basketball-related sports and offers individual health control. This algorithm uses a combination of wearable sensors, multi-view court video paths and physiological to create a three-level hierarchal data model, which is action-round-physiology. The algorithm is capable of balancing the energy expenditure of each round by calculating the energy consumed at high levels of intensity and recovering dynamic suggestions of training intensity and recovery on an individual basis depending on fatigue index, heart rate recovery curves, and muscle load thresholds and is based on continuous round-level energy consumption calculations, high-intensity scenario offsetting, and adaptive calibration of individual differences. The experimental findings indicate that the round-level energy consumption estimation model is able to represent the variations in the intensity of actions and offensive/defensive rhythm. Indicatively, with a high intensity round, P02 energy consumption was at 6.7 kcal round as compared to 6.0 kcal in a round of low intensity, which is a clear indication of exercise load variance. In the high-confrontation situations, the energy consumption correction mechanism also optimizes the calculation of the energy consumption. In an example, there is an increase in the energy consumption of round 1 of P02 of 6.7 kcal to 7.2 kcal after correction which is more representative of the real energy consumption of the continuous sprints and body contact.
Lin et al. (2026) studied this question.