| Popis: |
The quest for efficient and non-intrusive human activity recognition (HAR) in indoor environments has led to the burgeoning field of WiFi-based HAR. This method promises applications from healthcare monitoring to elderly care due to its cost-effectiveness and ease of deployment compared to traditional sensor-based systems. However, WiFi-based HAR faces significant challenges in maintaining performance across diverse environments and subjects, largely due to WiFi signal variability. Addressing this issue necessitates training models on extensive datasets. Recent studies have utilized conventional models, including Convolutional Neural Networks (CNNs), and sequence-to-sequence (Seq2Seq) models such as LSTM, GRU, or Transformer. Despite their precision, these models are computationally intensive and require more training data. To tackle these limitations, we propose a novel approach that leverages a Temporal Convolutional Network with Augmentations and Attention, referred to as TCN-AA. This model enhances computational efficiency and accuracy in the face of dataset variability by combining temporal convolutional layers with data augmentation strategies and an attention mechanism to focus on critical features. Our method is computationally efficient and significantly improves accuracy, even with a threefold increase in data size through augmentation techniques. Experiments on a public dataset indicate our approach outperforms state-of-the-art methods, achieving 99.42% accuracy. This work represents a significant step forward in the practical application of WiFi-based HAR, paving the way for broader adoption in critical areas like healthcare and security. |
