摘要：

中小学生课后常受游戏、短视频等诱惑, 且自制力尚未成熟, 自主学习易分心, 而家长难以全时监督, 导致学生学习成效不佳。为了提升学生的学习效率, 缓解家长焦虑情绪, 需要高可靠、低入侵的学习状态监测系统。在现有学习状态监测方法中, 基于计算机视觉和穿戴设备的方法存在依赖设备和环境、影响用户舒适度、侵犯个人隐私等弊端。针对上述问题, 以不同学习状态下的微小动作为识别目标, 将学习行为分为打游戏、阅读、写字、休息4种状态, 提出一种基于Wi-Fi信道状态信息(CSI)的非接触式学习状态监测方法Wi-LSM。该方法由Wi-Fi网卡采集CSI原始数据后, 首先利用相位校准与线性插值算法预处理数据, 以消除原始相位偏移并填补缺失数据包; 然后提取滤波降噪后幅值的时频域信息, 结合相位差共同形成识别特征; 最后将感知特征输入到所构建的多层机制卷积神经网络模型BN_SE_CNN中, 以实现不同学习状态的分类。实验结果表明, 该方法在不同室内环境下的最佳识别准确率达到96%, 验证了系统在学习状态监测方面的有效性。

关键词: 信道状态信息, 学习状态监测, 相位校准, 时频域, 卷积神经网络

Abstract:

Primary and secondary school students face numerous distractions such as games and short videos after class. They have limited self-control and tend to become distracted during autonomous learning. Constant supervision by parents is also difficult, which leads to poor learning outcomes. A highly reliable and low-intrusion learning state monitoring system is required to enhance students' learning efficiency and alleviate parents' anxiety. Among existing learning state monitoring methods, those based on computer vision and wearable devices have drawbacks such as relying on equipment and environment, affecting user comfort, and infringing personal privacy. To address these issues, this paper identifies micromotions under different learning states as the recognition target, categorizing learning behaviors into four states: gaming, reading, writing, and resting. The paper proposes a noncontact learning state monitoring method called Wi-LSM, based on Wi-Fi Channel State Information (CSI). The proposed method involves the following steps: first, the Wi-Fi network card collects raw CSI data, which are then preprocessed using phase calibration and linear interpolation algorithms to eliminate original phase shifts and fill missing data packets; second, the time-frequency domain information of filtered and denoised amplitudes is extracted and combined with phase differences to form recognition features; finally, perceptual features are input into a multilayer convolutional neural network model, BN_SE_CNN, to classify different learning states. Experimental results show that the method achieves an optimal recognition accuracy of 96% in different indoor environments, verifying the effectiveness of the system for learning state monitoring.

Key words: Channel State Information (CSI), learning state monitoring, phase correction, time-frequency domain, Convolutional Neural Network (CNN)