Human activity recognition (HAR) based on Wi-Fi signals has attracted significant attention due to its convenience and the availability of infrastructures and sensors. Channel State Information (CSI) measures how Wi-Fi signals propagate through the environment. However, many scenarios and applications have insufficient training data due to constraints such as cost, time, or resources. This poses a challenge for achieving high accuracy levels with machine learning techniques. In this study, multiple deep learning models for HAR were employed to achieve acceptable accuracy levels with much less training data than other methods. A pretrained encoder trained from a Multi-Input Multi-Output Autoencoder (MIMO AE) on Mel Frequency Cepstral Coefficients (MFCC) from a small subset of data samples was used for feature extraction. Then, fine-tuning was applied by adding the encoder as a fixed layer in the classifier, which was trained on a small fraction of the remaining data. The evaluation results (-fold cross-validation and = 5) showed that using only 30% of the training and validation data (equivalent to 24% of the total data), the accuracy was improved by 17.7% compared to the case where the encoder was not used (with an accuracy of 79.3% for the designed classifier, and an accuracy of 90.3% for the classifier with the fixed encoder). While by considering more calculational cost, achieving higher accuracy using the pretrained encoder as a trainable layer is possible (up to 2.4% improvement), this small gap demonstrated the effectiveness and efficiency of the proposed method for HAR using Wi-Fi signals.

中文翻译：

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使用听觉特征、自动编码器和微调，有效改进基于 Wi-Fi 的人类活动识别

基于 Wi-Fi 信号的人类活动识别 (HAR) 因其便利性以及基础设施和传感器的可用性而引起了广泛关注。信道状态信息 (CSI) 测量 Wi-Fi 信号如何在环境中传播。然而，由于成本、时间或资源等限制，许多场景和应用的训练数据不足。这对利用机器学习技术实现高精度水平提出了挑战。在这项研究中，采用了 HAR 的多个深度学习模型，以比其他方法更少的训练数据达到可接受的准确度水平。使用来自小数据样本子集的梅尔频率倒谱系数 (MFCC) 的多输入多输出自动编码器 (MIMO AE) 训练的预训练编码器用于特征提取。然后，通过将编码器添加为分类器中的固定层来进行微调，该编码器在剩余数据的一小部分上进行训练。评估结果（-fold交叉验证和= 5）表明，仅使用30％的训练和验证数据（相当于总数据的24％），与编码器的情况相比，准确率提高了17.7％未使用（设计的分类器的准确率为79.3％，带有固定编码器的分类器的准确率为90.3％）。虽然考虑更多的计算成本，使用预训练编码器作为可训练层实现更高的精度是可能的（最多提高 2.4%），但这一小差距证明了所提出的使用 Wi-Fi 信号的 HAR 方法的有效性和效率。