  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%.

Signal Processing;Computer Vision and Pattern Recognition;Machine Learning