Innovation in physical education teaching based on biomechanics feedback: Design and evaluation of personalized training programs
Abstract
Psychometric training is a learning and development process that is tied to the requirements and interests of individual learners. The effectiveness and the fun of workout practices can be greatly enhanced by using more personal methods in training. These programs entail tailoring workouts to the fitness level and aims of every participant, as well as their perception of the workout plans. The suggestion of this investigation is to develop and evaluate the effectiveness of individual physical training for teachers focused on biomechanics feedback. Proper body alignment is crucial during any exercise to avoid injuries and achieve maximum results; it is always difficult to sustain correct posture. For motion tracking and to give real-time biomechanics feedback to the students in this study, a refined convolutional neural network (RCNN) has been presented. Filming was done using high-speed cameras and motion capture systems to capture biomechanical responses, which included joint angle, muscle activation patterns, and body posture during activities. This study involved 158 participants drawn from different learning institutions. This approach offers rational and specific feedback on the postures of the body; it enables people to correct themselves and sustain motivation without engaging a trainer. In this study, participants with different levels of fitness engaged with the interactive system were compared to the traditional training method. The result indicated a positive shift in the delivery of personalized training with biomechanics feedback on the system’s potential teaching aid in physical education classes. The study, thus underscores the importance of technology supporting change in physical education programs to improve the student’s learning experiences and their performance in exercises.
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