WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal

• URL: http://arxiv.org/abs/2305.18211v2
• Date: Thu, 11 Jan 2024 09:02:16 GMT
• Title: WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal
• Authors: Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, and Timothy K. Shih
• Abstract summary: Wi-Fi based human activity recognition has gained considerable interest in recent times. A challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. We propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA.
• Score: 4.0773490083614075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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