基于物联网的电子学习试验台的实现:考虑四种脑电波的Mean-Shift聚类方法的性能评估

Keita Matsuo, M. Yamada, Kevin Bylykbashi, Miralda Cuka, Yi Liu, L. Barolli
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引用次数: 5

摘要

由于互联网提供的机会,人们正在利用电子学习课程,大量的研究工作已经致力于电子学习系统的开发。到目前为止,人们提出了许多电子学习系统并进行了实际应用。然而,在这些系统中,电子学习完成率很低。原因之一是学习欲望和动力不高。在这项工作中,我们提出了一个基于物联网的电子学习测试平台,使用安装在树莓派上的树莓派。我们和我们实验室的一个学生一起做了一些伽马型脑电波的实验。我们使用Mind Wave Mobile (MWM)来获取数据,并考虑了四种情况:睡眠、放松、活动和运动。然后,采用mean-shift聚类算法对数据进行聚类。评估结果表明,我们的测试平台可以通过delta, theta, gamma和alpha脑电波来判断人的情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of an IoT-Based E-Learning Testbed: Performance Evaluation Using Mean-Shift Clustering Approach Considering Four Types of BrainWaves
Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In this work, we present an IoT-Based E-Learning testbed using Raspberry Pi mounted on Raspbian. We carried out some experiments with a student of our laboratory for gamma type of brain waves. We used Mind Wave Mobile (MWM) to get the data and considered four situations: sleeping, relaxing, active and moving. Then, we used mean-shift clustering algorithm to cluster the data. The evaluation results show that our testbed can judge the human situation by using delta, theta, gamma and alpha brain waves.
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