Comparison of single-action recognition accuracy rate in MHAD dataset.
During the collection of sport posture data, participants perform specific football actions based on their exercise habits. These football postures consist of distinct upper limb actions and lower limb actions, which are recognized separately. As a result, separate classifiers are constructed for upper limb actions and lower limb actions. The recognition results of random forest classifiers, support vector machines, and Bayesian networks for upper and lower limb actions are presented in 