Can WiFi Estimate Person Pose?

TL;DR

This paper investigates whether WiFi signals can be used to estimate human pose, demonstrating a neural network approach that achieves promising results comparable to camera-based methods.

Contribution

The authors introduce WiSPPN, a novel fully convolutional network that estimates human pose from WiFi data, bridging WiFi sensing and vision tasks.

Findings

WiFi-based pose estimation is feasible with promising accuracy.

The proposed WiSPPN network effectively predicts keypoints from WiFi signals.

Evaluation on over 80,000 images shows positive results.

Abstract

WiFi human sensing has achieved great progress in indoor localization, activity classification, etc. Retracing the development of these work, we have a natural question: can WiFi devices work like cameras for vision applications? In this paper We try to answer this question by exploring the ability of WiFi on estimating single person pose. We use a 3-antenna WiFi sender and a 3-antenna receiver to generate WiFi data. Meanwhile, we use a synchronized camera to capture person videos for corresponding keypoint annotations. We further propose a fully convolutional network (FCN), termed WiSPPN, to estimate single person pose from the collected data and annotations. Evaluation on over 80k images (16 sites and 8 persons) replies aforesaid question with a positive answer. Codes have been made publicly available at https://github.com/geekfeiw/WiSPPN.