Student’s Health Exercise Recognition Tool for E-Learning Education

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 physical education needs especially in young learners. The proposed system focuses on necessary implementation recognition (SHER) using modified Quaternion-based filter for inertial data refining and fusion pre-processing steps. Further, cleansed has been segmented overlapping windowing approach followed by patterns identification form static kinematic signal patterns. Furthermore, these utilized extract cues both patterned signals, which are further optimized Fisher’s linear discriminant analysis (FLDA) technique. Finally, activities categorized extended Kalman (EKF)-based neural networks. This can be implemented establishments including intelligent training mentors, smart simulations, interactive management methods.