Quantum Convolutional Neural Networks based Human Activity Recognition using CSI

Human Activity Recognition (HAR) plays an important role in smart environments and Internet of Things (IoT) applications, where accurate real-time monitoring can enhance automation and interaction. While Classical Convolutional Neural Networks (CNNs) have been widely used for such tasks, they face limitations in scalability and feature extraction, especially when dealing with large datasets. To address these challenges, we propose a novel application of Quantum Convolutional Neural Networks (QCNNs) specifically tailored for processing CSI data in HAR. Our method aims to combine the power of quantum computing with classical convolutional methods to extract complex patterns from CSI signals, thereby improving accuracy and maintaining computational efficiency. The challenge of high-dimensional CSI data is addressed by hybridizing classical and quantum layers in the architecture, which enables better feature extraction and learning capabilities. Results show that our QCNN achieves superior performance in comparison to traditional CNNs.