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

2025-11-21

AI Summary

Emerging applications in gaming, smart homes, and automotive systems demand non-contact, privacy-preserving, and low-cost natural human–computer interaction. Method: This paper introduces the first approach to repurpose commercial Qi wireless chargers as passive gesture sensors by exploiting inherent electromagnetic field perturbations—requiring no additional hardware or cameras. We develop an end-to-end gesture recognition framework comprising electromagnetic signal modeling, time-frequency feature extraction, and a lightweight, robust classification model. Contribution/Results: Extensive evaluation across 30 users, 10 device types, and 5 Qi charger models achieves a mean recognition accuracy of 97.2%. User studies confirm high usability, effortless deployment, and strong privacy guarantees. This work establishes a novel sensing paradigm leveraging existing wireless charging infrastructure, enabling scalable, cost-effective, and privacy-secure ubiquitous interaction.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Contactless gesture recognition using wireless charger EM signals
• Overcoming limitations of physical buttons and camera-based systems
• Achieving high accuracy interaction without privacy concerns

Innovation

• Uses wireless charger EM signals for gesture recognition
• Employs robust classification model for accurate interpretation
• Achieves high accuracy with privacy-conscious contactless interaction