利用机器学习的CSI感知实用性验证
Validation of Practicality for CSI Sensing Utilizing Machine Learning
AI总结：
本研究利用WLAN的CSI数据开发五种人体姿态识别机器学习模型，发现两类模型在原场景准确率超85%，但跨环境时降至约30%，揭示了CSI感知模型空间泛化能力不足的关键挑战。
摘要：
在本研究中，我们将WLAN通信中常用的信道状态信息（Channel State Information，CSI）用作训练数据，开发并评估了五种用于识别人体姿态（站立、坐姿、躺卧）的不同机器学习模型。我们采用的模型包括：(i) 线性判别分析、(ii) 朴素贝叶斯-支持向量机、(iii) 核-支持向量机、(iv) 随机森林以及(v) 深度学习。我们系统分析了这些模型的准确率如何随训练数据量的不同而变化。此外，为评估其空间泛化能力，我们在与数据采集环境不同的场景中评估了模型的性能。实验结果表明，尽管(ii) 朴素贝叶斯-支持向量机和(v) 深度学习这两个模型在原始场景中达到了85%及以上的准确率，但在应用于不同环境时，其准确率降至约30%。这些结果凸显出，尽管基于CSI的机器学习模型在一致的空间结构中可实现高准确率，但随着空间条件的变化，其性能会大幅下降，这也凸显了其泛化能力方面的一项重大挑战。
英文摘要：
In this study, we leveraged Channel State Information (CSI), commonly utilized in WLAN communication, as training data to develop and evaluate five distinct machine learning models for recognizing human postures: standing, sitting, and lying down. The models we employed were: (i) Linear Discriminant Analysis, (ii) Naive Bayes-Support Vector Machine, (iii) Kernel-Support Vector Machine, (iv) Random Forest, and (v) Deep Learning. We systematically analyzed how the accuracy of these models varied with different amounts of training data. Additionally, to assess their spatial generalization capabilities, we evaluated the models' performance in a setting distinct from the one used for data collection. The experimental findings indicated that while two models -- (ii) Naive Bayes-Support Vector Machine and (v) Deep Learning -- achieved 85% or more accuracy in the original setting, their accuracy dropped to approximately 30% when applied in a different environment. These results underscore that although CSI-based machine learning models can attain high accuracy within a consistent spatial structure, their performance diminishes considerably with changes in spatial conditions, highlighting a significant challenge in their generalization capabilities.
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