School of Preschool Education, Xi'an University, Xi'an 710065, Shaanxi, China.
Contrast Media Mol Imaging. 2022 Sep 5;2022:3775800. doi: 10.1155/2022/3775800. eCollection 2022.
Along with the main feedback information of early childhood education activities, children's movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children's sports evaluation. Traditional physical education overemphasizes the leading role of teachers, and young children can only passively receive the education of teachers. Teachers always use the same standard to evaluate each child, simply assigning a rating of "strong," "moderate," and "poor" to children, ignoring the differences in children's physical intelligence. The emergence of multidata fusion technology can use the differences and complementarity of various data in evaluation functions to make up for the insufficiency of a single exercise evaluation result, purposefully choose the evaluation method flexibly according to the content of the exercise and the scene of the exercise, and gradually improve the level of children's exercise evaluation. This paper studied the children's motor intelligence evaluation system based on multidata fusion. To this end, this paper will focus on children's autonomous sports games and determine the first-level indicators in the children's sports evaluation index system as four indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness. It used the principle of multidata fusion, firstly evaluates children's exercise physiology data through a fuzzy neural network algorithm, and then combines the adaptive weighted data fusion algorithm with D-S evidence theory to evaluate children's movement intelligence. Experiments showed that the multidata evaluation system can take effective measures to intelligently evaluate children's comprehensive motor ability. Compared with the traditional evaluation method, the evaluation results are increased by 5%, and the children's sports evaluation results are more average, which can enhance children's sports confidence and promote children's effective exercise.
除了早期儿童教育活动的主要反馈信息外，儿童运动评估在早期儿童教育活动中起着重要的指导作用。教师可以根据儿童运动评估的结果，因材施教。传统的体育教育过分强调教师的主导作用，幼儿只能被动接受教师的教育。教师总是用同样的标准来评价每个孩子，简单地给孩子一个“强”、“中”、“弱”的评级，忽略了孩子身体智力的差异。多数据融合技术的出现，可以利用评价功能中各种数据的差异和互补性，弥补单一运动评价结果的不足，根据运动内容和运动场景有针对性地灵活选择评价方法，逐步提高儿童运动评价水平。本文研究了基于多数据融合的儿童运动智能评价系统。为此，本文将重点关注儿童自主运动游戏，并确定儿童运动评估指标体系中的一级指标为四项：课堂表现、身体素质、运动技能和课外健身。它采用多数据融合原理，首先通过模糊神经网络算法对儿童运动的生理数据进行评估，然后将自适应加权数据融合算法与 D-S 证据理论相结合，对儿童的运动智能进行评估。实验表明，多数据评价系统可以采取有效措施，智能地评价儿童的综合运动能力。与传统评价方法相比，评价结果提高了 5%，儿童运动评价结果更加平均，可以增强儿童的运动信心，促进儿童的有效运动。
