Analyzing Running Patterns in Track and Field Events Using Deep Learning

To better understand how athletes perform and how to train, it is crucial to analyze their running patterns in track and field competitions. Pattern recognition algorithms developed in the past typically depend on human observers, which has limitations in objectivity and processing speed. This research presents a new automatic running pattern analysis method that uses Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The optimum network architecture for analyzing athletes' motions frame by frame in video footage is a bi-LSTM network because it models sequential data and captures temporal dependencies. A strong and effective system for real-time pattern recognition and classification of different types of running, such as sprinting, jogging, and hurdling, is the goal of this research. The module run extensive experiments to test how well the Bi-LSTM-based model performs on various datasets containing various track and field events. The results show that the suggested method is reliable and successful at spotting running patterns in multiple contexts. In addition, coaches and athletes can use the proposed approach to optimize training regimens and improve performance by offering insights into biomechanical aspects. This work uses deep learning to further sports science by giving a data-driven, objective way to analyze track and field runners' performance patterns. With this new system, can finally say goodbye to guesswork when measuring and improving athletic performance. This will lead to better training plans and better results in competition.