With China’s comprehensive development of information technology,it is now widely used in the field of higher vocational education,and online teaching has emerged at a critical juncture.In the information age,there a...With China’s comprehensive development of information technology,it is now widely used in the field of higher vocational education,and online teaching has emerged at a critical juncture.In the information age,there are challenges in meeting the actual needs of contemporary vocational students with traditional teaching methods,along with higher vocational training and school-enterprise cooperation and enrollment work taking place.Online teaching can stimulate students’interest in learning,break the limitation of time and space in conventional teaching,as well as improve the teaching efficiency.Therefore,taking higher vocational colleges in Chongqing as an example,this study explores the integration of online teaching into public physical education courses,in hope to provide some reference for higher vocational colleges.展开更多
Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual education.Automated student health exercise is a di...Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual education.Automated student health exercise is a difficult task but an important one due to the physical education needs especially in young learners.The proposed system focuses on the necessary implementation of student health exercise recognition(SHER)using a modified Quaternion-basedfilter for inertial data refining and data fusion as the pre-processing steps.Further,cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal patterns.Furthermore,these patterns have been utilized to extract cues for both patterned signals,which are further optimized using Fisher’s linear discriminant analysis(FLDA)technique.Finally,the physical exercise activities have been categorized using extended Kalmanfilter(EKF)-based neural networks.This system can be implemented in multiple educational establishments including intelligent training systems,virtual mentors,smart simulations,and interactive learning management methods.展开更多
基金Project of“Innovation of Teaching Mode of Public Physical Education in Chongqing”(Project Number:203675).
文摘With China’s comprehensive development of information technology,it is now widely used in the field of higher vocational education,and online teaching has emerged at a critical juncture.In the information age,there are challenges in meeting the actual needs of contemporary vocational students with traditional teaching methods,along with higher vocational training and school-enterprise cooperation and enrollment work taking place.Online teaching can stimulate students’interest in learning,break the limitation of time and space in conventional teaching,as well as improve the teaching efficiency.Therefore,taking higher vocational colleges in Chongqing as an example,this study explores the integration of online teaching into public physical education courses,in hope to provide some reference for higher vocational colleges.
基金supported by a Grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual education.Automated student health exercise is a difficult task but an important one due to the physical education needs especially in young learners.The proposed system focuses on the necessary implementation of student health exercise recognition(SHER)using a modified Quaternion-basedfilter for inertial data refining and data fusion as the pre-processing steps.Further,cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal patterns.Furthermore,these patterns have been utilized to extract cues for both patterned signals,which are further optimized using Fisher’s linear discriminant analysis(FLDA)technique.Finally,the physical exercise activities have been categorized using extended Kalmanfilter(EKF)-based neural networks.This system can be implemented in multiple educational establishments including intelligent training systems,virtual mentors,smart simulations,and interactive learning management methods.
