首页|LD-identify:基于无源RFID的网络学习状态识别

LD-identify:基于无源RFID的网络学习状态识别

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在线教育中，学生实时动作能够准确反映学生当前的学习状态，在不影响学习注意力和保证个人隐私信息安全的情况下，准确识别学习动作是监测在线教育质量的关键要素。对此，提出一种基于无源RFID的网络学习动作识别系统LD-identify。LD-identify仅通过射频信号完成学生动作识别，所以识别系统可以很好地保护个人的隐私信息，且避免设备昂贵等一系列问题。通过提取相位和信号强度的有效特征和深度学习算法，LD-identify能够获得很好的识别准确率的性能。实验表明，LD-identify只需要在帽子的背面粘贴两个射频标签，就能很好地识别出抬头低头、左右摇头、前倾后倾3种动作。为了进一步验证系统性能，研究6名志愿者在不同的场景中的动作识别的准确率，实验结果显示LD-identify能够在不同的场景下很好地识别所有用户的3种动作，利用卷积神经网络构建分类模型来识别动作可以取得很好的识别率，识别准确率达到95。5%以上。
LD-identify:Network learning state recognition based on passive RFID
In online education,students'real-time movements can accurately reflect their current learning state.In the case that it does not affect the study attention and ensure the security of personal privacy information,accurate identification of learning actions is a key factor in monitoring the quality of online education.This paper proposes a network learning action recognition system LD-identify based on passive RFID.LD-identify only uses radio frequency signals to complete student movement identification,so the identification system can protect personal privacy information well and prevent a series of problems such as expensive equipment.By extracting effective features of phase and signal strength,LD-Identify can achieve good performance of recognition accuracy with deep learning algorithm.The experiment shows that only two radio frequency tags sticked on the back of the hat can well identify three movements:looked up and down,shake your head around,and leaning backward.In order to further verify the performance of the system,the accuracy of six volunteers'action recognition in different scenes is investigated.The experimental results show that LD-Identify can well identify three actions of all users in different scenarios,the convolutional neural network is used to construct a classification model to recognize actions and achieve good recognition rate,and the recognition accuracy reaches more than 95.5%.

radio frequency identificationaction recognitionmachine learningdeep learningthe labelmechanism of attention

王涛春、邱庆、王成田、陈付龙

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安徽师范大学计算机与信息学院,安徽芜湖 241002

安徽师范大学安徽省医疗大数据智能系统工程研究中心,安徽芜湖 241002

无线射频识别 动作识别 机器学习 深度学习 标签 注意力机制

安徽省重点研究与开发计划项目安徽省自然科学基金项目国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目安徽省质量工程项目

2022a050200492108085MF219619724396197243861871412020jyxm0677

2024

10.13195/j.kzyjc.2022.0622

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子：1.227
ISSN：1001-0920
年,卷(期)：2024.39(1)
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