MVLSC Home · Issue Contents · Forthcoming Papers
Artificial Intelligence and Fuzzy Logic Approach Based Aerobic Physical Education Training in Colleges
Jian Wan
Aerobic physical education training in colleges helps to reduce mental stress, improves the students’ concentration, etc. The sessions allocated for physical education are limited compared to the classrooms/ subject-oriented allocations. Therefore, the training efficiency improvements rely on the maximum possible analysis pursued in the limited sessions. Considering the time constraint and maximum efficiency across multiple physical education sessions, this article introduces a Training Assessment Method (TAM) for Physical Education (PE). The proposed method identifies two perspectives namely: the time constraints and output efficiency from the training sessions. This identification is performed using two-level fuzzification. The fuzzification process identifies the maximum and minimum efficiency deviations in training sessions. The identified deviations are balanced by allocating additional sessions that are further investigated using state learning. This state learning verifies the improvements in training or retention based on min-max fuzzification outputs. Therefore, the available data from the sessions are presumably utilized for the above two perspectives for recommending time demand or modification. The maximum state retention implies only the efficiency other than the constraints observed. This method, therefore, improves the recommendations and reduces constraint issues for specific training.
Keywords: Data analysis, fuzzy logic, physical education training, state learning