2.7 Acquisition of ball-kicking data of players
The deep learning (DL) method aligns with the concept of an artificial neural network (ANN), which serves as a machine learning architecture. The neural network, functioning as an algorithm, trains the weights connecting individual units within the network. Drawing inspiration from the workings of the human brain, ANN algorithms have the capacity to learn and adapt to new scenarios. In the human brain, input signals are received and processed through the nervous system, while external stimuli are sensed through neurons that convert electrical signals from nerve endings. DL-based neural networks are mathematical models that emulate the neural system of the human brain. These networks exhibit high fault tolerance, fast learning and self-adaptation rates, and the ability to approximate nonlinear functions. They can be effectively employed for tasks such as binary image recognition, prediction, and fuzzy control of binary images. The BPNN is an example of a three-layer feedforward neural network comprising the input, hidden, and output layers. The structure of the BPNN is illustrated in 