Neural Networks for Effective Member Density Monitoring in IoT-Enabled Smart Fitness Centers

Improving user experience and operational management in the quickly growing field of IoT-enabled smart fitness centers relies heavily on accurate member density monitoring. This paper provides a new method that uses neural networks to effectively monitor member density in these kinds of settings. Incorporating Internet of Things (IoT) sensors into the fitness center's layout to gather occupancy data in real-time is essential to our process. Feed this data into a neural network architecture that was built to estimate densities. It demonstrates the accuracy, scalability, and durability of our system in various operating circumstances by evaluating its performance via real-world experiments and comprehensive simulations. It communicates about how our method may be used in smart fitness centers that are connected to the IoT to improve management efficiency, resource allocation, and user happiness. Our efforts help push the current state of the art in the use of artificial intelligence methods for improving the efficiency of advanced gyms.