A learning support system for 9 #x00D7; 9 multiplication table with Kinect

机译：带Kinect的9×9乘法表的学习支持系统

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We propose a learning support system for the 9 × 9 multiplication table “KuKu” and have implemented it using Kinect motion capture to recognize “air characters” written by the body actions of KuKu learners. We explain how to implement the system, report the results of a trial with elementary school students, and suggest the future directions of the system.

机译：我们为9×9乘法表“ KuKu”提出了一个学习支持系统，并使用Kinect运动捕捉实现了该系统，以识别KuKu学习者的肢体动作所书写的“空中人物”。我们将说明如何实施该系统，向小学生报告试验结果，并提出该系统的未来发展方向。

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《IEEE Global Conference on Consumer Electronics》|2013年|253-257|共5页
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Akazawa Noriko; Takei Yuki; Nakayama Yasuichi; Kakuda Hiroyasu;
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Kinect; body action; educational technology;

机译：Kinect;身体动作;教育技术;

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专利

1. IMMERSIVE LEARNING SUPPORT SYSTEM BASED ON KINECT SENSOR FOR CHILDREN TO LEARN ABOUT PALEONTOLOGICAL ENVIRONMENTS [J] . T. Nakayama, R. Yoshida, T. Nakadai, International Journal on Smart Sensing and Intelligent Systems . 2015,第2期

机译：基于kinect传感器的沉浸式学习支持系统，供儿童学习古生物学环境
2. Hybridized estimations of support vector machine free parameters C and gamma using a fuzzy learning strategy for microphone array-based speaker recognition in a Kinect sensor-deployed environment [J] . Ding Ing-Jr, Shi Jia-Yi Multimedia Tools and Applications . 2017,第23期

机译：Kinect传感器部署环境中基于麦克风阵列的说话人识别的模糊学习策略的支持向量机自由参数C和γ的混合估计
3. Look-up table-based large finite field multiplication in memory constrained cryptosystems [J] . Hasan M.A. IEEE Transactions on Computers . 2000,第7期

机译：内存受限密码系统中基于查询表的大有限域乘法
4. A learning support system for 9 #x00D7; 9 multiplication table with Kinect [C] . Akazawa Noriko, Takei Yuki, Nakayama Yasuichi, IEEE Global Conference on Consumer Electronics . 2013

机译：具有Kinect的9×9乘法表的学习支持系统
5. The Relationship of Multiplication Tables and Fraction Operations to Learning Algebra. [D] . Willis, Allen Kent. 2011

机译：乘法表和分数运算与学习代数的关系。
6. Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training [O] . Elise Klæbo Vonstad, Xiaomeng Su, Beatrix Vereijken, 2020

机译：基于深度学习的姿态估计系统对基于标记和Kinect系统的平衡训练
7. Figure 3: Third task setup: (A) Block reaching task. In each trial, the participant places their right hand to the side of the rightmost line and then moves until reaching the black block. Six precision motion capture cameras are attached one to each pillar. The user wears the Oculus Rift HMD and the Kinect is positioned in front of the table. Three task stages (B) as captured by the Oculus plus Kinect (C) and OptiTrack (D) systems. [O] . -1

机译：图3：第三任务设置：（a）块到达任务。在每次试验中，参与者将其右手放在最右边线的一侧，然后移动直到到达黑色块。六个精密运动捕获摄像机连接到每个支柱。用户佩戴Oculus Rift HMD，Kinect位于桌子前面。由Oculus Plus Kinect（C）和Optitrack（D）系统捕获的三个任务阶段（b）。

A learning support system for 9 #x00D7; 9 multiplication table with Kinect

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