Human pose estimation holds tremendous potential in fields such as human-computer interaction, motion capture, and virtual reality, making it a focus in human perception research. However, optical image-based pose estimation methods are often limited by lighting conditions and privacy concerns. Therefore, the use of wireless signals that can operate under various lighting conditions and obstructions while ensuring privacy is gaining increasing attention for human pose estimation. Wireless signal-based pose estimation technologies can be categorized into high-frequency and low-frequency methods. These methods differ in their hardware systems, signal characteristics, noise processing, and deep learning algorithm design based on the signal frequency used. This paper highlights research advancements and notable works in human pose reconstruction using millimeter-wave radar, through-wall radar, and WiFi. It analyzes the advantages and limitations of each signal type and explores potential research challenges and future developments in the field.
