CSI-Channel Spatial Decomposition for WiFi-Based Human Pose Estimation in Rescue Scenarios

In rescue scenarios, locating and understanding human motion quickly can make the difference between a successful intervention and a delayed response. Yet cameras, LiDAR, and wearable sensors often struggle in smoke-filled rooms, collapsed buildings, dark environments, or areas where direct line of sight is blocked. This is where WiFi sensing offers a promising alternative. By analyzing how human movement alters wireless signal propagation, it becomes possible to estimate body posture without requiring a camera-based view of the person.

Human Identification Procedure

CSI is a measurement that represents these disparities between carriers and subcarriers. The signal received by the receiver can be expressed using the CSI as follows.

The proposed framework uses Channel State Information (CSI) as the main sensing source. Human presence and motion affect the wireless channel, creating measurable variations in signal amplitude and phase. These raw CSI signals are first collected and then passed through a structured preprocessing pipeline that improves signal quality and extracts the most relevant motion information. This includes antenna selection, outlier removal, discrete wavelet transform (DWT)-based processing, and feature extraction. The goal is to decompose the spatial and temporal characteristics of the WiFi channel into informative representations that can support robust pose estimation even in complex indoor environments.

Human Identification Procedure

It begins with CSI raw data acquisition, where both amplitude and phase are extracted from the WiFi signals. These measurements are then refined through data preprocessing and transformed into features suitable for learning. Finally, the neural network predicts the human skeletal configuration and reconstructs a 3D human body representation. This pipeline shows how wireless sensing can move beyond simple activity recognition toward detailed human pose recovery.

At the core of the method is a neural network architecture designed to reconstruct the human skeleton from the processed CSI data. The model combines an encoder-decoder structure with a Spatial Orientation Attention Module (SOA), allowing it to better capture body orientation and motion relationships across the wireless channel. This enables the system to infer a structured human pose representation and map it to a full-body model such as SMPL-X, providing a richer reconstruction of the person’s posture.

Workflow for WiFi-based human pose estimation using CSI data and neural networks.

On the left, CSI data collection combined with camera-based pose information is shown, where multiple wireless links capture signal variations caused by a human subject. In the center, the collected data is used for neural network design, which learns the relationship between CSI measurements and human body posture. The output of the network is a human skeleton reconstruction and SMPL-X body model, shown as both a skeletal representation and a 3D human figure. On the right, the figure highlights the dataset structure, including a sample 3×3 CSI matrix pose skeleton and the corresponding CSI amplitude signal, illustrating how wireless signal patterns are linked to body movement and posture.

Experimental Setup

The experimental setup and example outputs. It shows sample skeleton predictions alongside CSI amplitude signals captured across multiple wireless links. These examples demonstrate how different human poses, such as walking or crouching, produce distinguishable channel responses. The comparison between ground-truth and predicted skeletons provides visual evidence that CSI-based sensing can track meaningful posture changes with good spatial consistency. This approach is especially relevant for search-and-rescue operations, where first responders need situational awareness in environments that are unsafe or visually inaccessible. A WiFi-based human pose estimation system could help detect trapped survivors, infer whether a person is standing, lying down, or crouched, and support faster decision-making without intrusive sensing hardware. By leveraging existing wireless infrastructure, the method also opens the door to low-cost, privacy-preserving monitoring in emergency response, healthcare, and smart building applications.

Experimental examples show the relationship between CSI amplitude variations and predicted skeletal motion under different activities.

Takeaway

CSI-channel spatial decomposition shows that WiFi signals can be transformed into meaningful human pose information, enabling camera-free and privacy-preserving body reconstruction in difficult rescue environments. By combining CSI preprocessing, spatial feature learning, and lightweight neural modeling, this approach offers a practical path toward detecting and understanding human posture in smoke, darkness, debris, or non-line-of-sight conditions where conventional vision systems often fail.

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