The Wireless Charger as a Gesture Sensor: A Novel Approach to Ubiquitous Interaction

TL;DR

EMGesture demonstrates that electromagnetic signals emitted by Qi wireless chargers can serve as a robust, contactless gesture sensor for pervasive interaction. The system collects gesture-perturbed EM fields with a wideband antenna and SDR, then processes the data through averaged short-window FFT, Variational Mode Decomposition denoising, and a Random Forest classifier to achieve high recognition accuracy. It delivers 97.59% accuracy across nine gestures and proves robustness across users, devices, and environments, with a user study confirming usability and privacy advantages. The work also explores active modulation via charger negative feedback as a proof-of-concept for future richer EM-based interactions, highlighting potential extensions to smart homes, automotive interfaces, and assistive technologies.

Abstract

Advancements in information technology have increased demand for natural human-computer interaction in areas such as gaming, smart homes, and vehicles. However, conventional approaches like physical buttons or cameras are often limited by contact requirements, privacy concerns, and high costs.Motivated by the observation that these EM signals are not only strong and measurable but also rich in gesture-related information, we propose EMGesture, a novel contactless interaction technique that leverages the electromagnetic (EM) signals from Qi wireless chargers for gesture recognition. EMGesture analyzes the distinctive EM features and employs a robust classification model. The end-to-end framework enables it capable of accurately interpreting user intent. Experiments involving 30 participants, 10 mobile devices, and 5 chargers showed that EMGesture achieves over 97% recognition accuracy. Corresponding user studies also confirmed higher usability and convenience, which demonstrating that EMGesture is a practical, privacy-conscious, and cost-effective solution for pervasive interaction.

Figures (17)

• Figure 1: Electromagnetic signals under different wireless charger states and different gestures
• Figure 2: EMGesture overall framework diagram
• Figure 3: Average power spectrum solution process
• Figure 4: Hardware components of the EMGesture prototype.
• Figure 5: Custom ESP32-based electromagnetic signal collection board