The utilization of Wi-Fi-based human activity recognition (HAR) 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, and others. 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%.

提出了一种新颖的基于 TCN-AA 的方法，该方法结合增强和注意力机制，具有更高的计算效率和准确率，在公共数据集上的表现优于现有技术，最终准确率为 99.42%。