Design of intelligent assistive system for physical education: based on personalized training plan

Abstract

Instead of viewing different components in solitude, teachers' opinion with respect and pedagogical knowledge demands a comprehension of the relationships connecting them. The instructor must be knowledgeable about the subject matter, comprehend the most effective delivery methods for the information, be acquainted with the traits of the children, and be aware of the academic setting to educate in that field effectively. Sport is still undergoing economic, cultural, and ethical change. On the other side, throughout the past century, science has been the sport's most pervasive transformation. Players can now jump higher, run faster, and, importantly—retain their health due to scientific understanding. Even though scholars, organizations, and governments have indeed encouraged physical education instructors to incorporate software in their classes, technology is often employed for routine duties, such as tracking enrollment and assessing, recording, and reporting children’s' progress. In order to estimate posture in physical training, this research proposes a continuous filter convolution neural network. The approach also assesses the learners' understanding, memory, and accomplishments and offers suggestions for enhancements and remedial actions. The framework and conventional teaching-learning methodologies are then compared characteristics per aspect for output criteria. Finally, the classification algorithm is contrasted with other deep learning algorithms, and it is found that the proposed ConFil_CNN achieves 98.5% accuracy, 96.7% of precision, 93.5% of recall, 95.2% of F1-score and 12.4ms of response time.