The utilization of Wi-Fi based human activity recognition has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequence-to-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.

翻译：近年来，基于WiFi的人体活动识别因其在医疗监测（如呼吸与心率）、安防及老年人护理等领域的应用而备受关注。相比依赖摄像头和传感器的传统先进技术，这些基于WiFi的方法具有成本更低、部署更简便等优势。然而，基于WiFi的人体活动识别面临一项重大挑战：当场景或对象发生变化时，性能显著下降。为缓解该问题，需使用大规模数据集训练模型。近期研究普遍采用基于CNN的模型或序列到序列模型（如LSTM、GRU或Transformer）。尽管序列到序列模型精度更高，但其计算负担更重且需要更多训练数据。针对这些局限性，我们提出一种新颖方法，利用结合数据增强与注意力机制的时间卷积网络（记为TCN-AA）。该方法计算高效，且通过我们的增强技术将数据量扩展三倍后仍能保持更高精度。在公开数据集上的实验表明，该方法优于现有最先进技术，最终准确率达到99.42%。