2.6 Football players’ posture recognition using machine learning
’s hand gestures, ’ posture
Data on acceleration and angular velocity were collected from football players’ positions. Similarly, Eq (The data acquisition module captures three acceleration vectors and three angular velocity vectors. The vector sum of acceleration and angular velocity is computed using Eqs (Eq (The time-domain features extracted comprise a total of 16 dimensions of attitude parameters. These include:Acceleration sensors: x-axis, y-axis, z-axis, and the mean value of the acceleration vector sum.Angular velocity vector sensors: x-axis, y-axis, z-axis, and mean value of the angular velocity vector sum.Acceleration sensors: x-axis, y-axis, z-axis, and variance of the acceleration vector sum.Angular velocity vector sensors: x-axis, y-axis, z-axis, and variance of the angular velocity vector sum.These 16 dimensions provide valuable information about the attitude and movement characteristics of football players.Following feature extraction, a 32-dimensional feature parameter set is generated for football players’ posture recognition. However, to improve the recognition performance and efficiency of the classifier, it is important to filter out irrelevant or redundant feature parameters that are closely related to the football player’s hand gestures. This can be achieved by selecting relevant features to reduce the dimensionality of the data. In this work, the principal component analysis (PCA) method was employed for feature parameter selection after conducting experimental tests. PCA helps identify the most informative features and reduce the dimensionality of the dataset, thereby enhancing the accuracy and efficiency of football hand gesture recognition.