JCDR‐TCN: Joint Channel Dimensionality Reduction and Temporal Convolutional Network for First‐Person Perspective Gesture Recognition
Wei Zheng, Xiaofan Li, Zhengjun Fu, Zongxing Zhao, Qizhi Yuan, Kaihong Chen · Electronics Letters · 2025
ABSTRACT WiFi‐based gesture recognition is a promising privacy‐preserving sensing modality. Existing research has predominantly relied on fixed equipment configurations from third‐person perspective, inherently limiting deployment flexibility and interaction possibilities. Gesture recognition from first‐person perspective, using wearable devices, offers a superior alternative. However, its deployment is critically hindered by the high computational overhead of current deep learning models on embedded or mobile devices. To address the problems, this paper designed joint channel dimensionality reduction and temporal convolutional network (JCDR‐TCN), a lightweight model that integrates channel dimensionality reduction and a TCN for first‐person gesture recognition. Raw data are collected using a Raspberry Pi 4B, processed and then classified by JCDR‐TCN. Evaluated on a self‐collected first‐person perspective dataset, our JCDR‐TCN achieves a recognition accuracy of 95.63%. Simultaneously, the model only contains 0.05 million parameters and requires 8.38 million floating‐point operations (FLOPs), demonstrating a favorable trade‐off between accuracy and computational efficiency, thereby making it well‐suited for embedded and mobile deployment.