EMGesture: Wireless Charger as Gesture Sensor for Ubiquitous Interaction

Table of Contents

1 Introduction

The global human-machine interface market is projected to reach USD 7.24 billion by 2026, with consumers increasingly demanding natural and intelligent interaction methods. Current interaction modalities face significant limitations: contact-based approaches like touchscreens struggle in humid environments and have high costs, while contactless methods like cameras raise privacy concerns and voice interaction has limited command comprehension.

EMGesture addresses these challenges by leveraging the electromagnetic signals emitted by Qi-standard wireless chargers for gesture recognition. This approach transforms existing charging infrastructure into ubiquitous gesture sensors, eliminating the need for additional hardware while maintaining user privacy.

2 EMGesture System Design

2.1 Electromagnetic Signal Analysis

The system captures EM signals generated during wireless charging operations. When gestures are performed near the charging surface, they cause measurable disturbances in the electromagnetic field. The key insight is that different gestures produce distinctive EM patterns that can be classified using machine learning algorithms.

The signal processing pipeline involves:

• Raw EM signal acquisition from charger coils
• Noise filtering and signal preprocessing
• Feature extraction including amplitude, frequency, and phase characteristics
• Pattern recognition using supervised learning

2.2 Gesture Recognition Framework

EMGesture employs an end-to-end classification model that processes EM signal features to identify user gestures. The framework includes data collection, feature engineering, model training, and real-time inference components. The system supports common gestures including swipes, taps, circles, and custom patterns.

3 Experimental Results

3.1 Performance Metrics

Comprehensive experiments involving 30 participants, 10 mobile devices, and 5 different wireless chargers demonstrated EMGesture's robust performance:

• Overall Accuracy: 97.2% across all tested scenarios
• False Positive Rate: < 2.1% under normal operating conditions
• Latency: Average recognition time of 120ms
• Device Compatibility: Consistent performance across different smartphone models and charger brands

3.2 User Study Findings

User studies confirmed higher usability and convenience compared to traditional interaction methods. Participants reported:

• 85% preference over touchscreen in kitchen environments
• 92% satisfaction with privacy aspects compared to camera-based systems
• 78% found the system intuitive after minimal training

4 Technical Analysis

Core Insight

EMGesture represents a paradigm shift in ubiquitous computing—turning passive charging infrastructure into active sensing platforms. This isn't just another gesture recognition system; it's a fundamental rethinking of how we can leverage existing electromagnetic emissions for dual-purpose functionality. The approach demonstrates remarkable ingenuity by recognizing that the very EM interference traditionally considered as noise can become the signal for interaction.

Logical Flow

The technical progression is elegantly simple: Qi chargers emit predictable EM fields → hand gestures create measurable perturbations → machine learning models map these perturbations to specific gestures → real-time classification enables interaction. This flow eliminates the need for additional sensors, leveraging infrastructure that's already becoming ubiquitous in homes, vehicles, and public spaces.

Strengths & Flaws

Strengths: The privacy-preserving nature is revolutionary—unlike camera-based systems that capture detailed visual data, EM signals only reveal gesture patterns. The cost-effectiveness is undeniable, requiring zero additional hardware. The 97% accuracy rivals dedicated gesture recognition systems while using existing infrastructure.

Flaws: The limited gesture vocabulary compared to camera systems is concerning. The range constraints (must be near charger) severely limit application scenarios. The system's performance across different environmental conditions and charger qualities remains questionable. Like many academic prototypes, the real-world robustness under electromagnetic interference from other devices is untested.

Actionable Insights

Manufacturers should immediately integrate this technology into next-generation wireless chargers. The automotive industry represents the low-hanging fruit—integrating EM gesture control into car wireless chargers could revolutionize in-vehicle interaction while maintaining driver focus. Smart home developers should prototype kitchen applications where traditional touch interfaces fail. The research community must address the range limitations and expand the gesture vocabulary.

Technical Formulation

The gesture recognition can be mathematically represented as a classification problem where the system learns a mapping function $f: X \\rightarrow Y$ from EM signal features $X$ to gesture classes $Y$. The EM signal disturbance $\\Delta S$ caused by a gesture can be modeled as:

$$\\Delta S(t) = A(t) \\cdot \\sin(2\\pi f_c t + \\phi(t)) + n(t)$$

where $A(t)$ represents amplitude modulation, $f_c$ is the carrier frequency, $\\phi(t)$ is phase variation, and $n(t)$ represents noise. The classification model employs feature vectors extracted from $\\Delta S(t)$ including spectral features, temporal patterns, and amplitude characteristics.

Analysis Framework Example

Case Study: Kitchen Environment Implementation

In a smart kitchen scenario, a wireless charger embedded in the countertop can detect gestures for controlling appliances. The analysis framework involves:

1. Signal Baseline Establishment: Capture EM signature of idle charger state
2. Gesture Library Definition: Map specific gestures to kitchen commands (circular motion for volume control, swipe for brightness adjustment)
3. Environmental Adaptation: Account for metal interference from appliances
4. User Customization: Allow personal gesture training for frequently used functions

5 Future Applications

The potential applications of EMGesture technology extend across multiple domains:

• Automotive: Gesture control for infotainment systems using built-in wireless chargers
• Healthcare: Contactless control in sterile environments and for mobility-impaired users
• Smart Homes: Kitchen appliance control, lighting adjustment, and media control
• Industrial: Maintenance-free control interfaces in manufacturing environments
• Public Spaces: Interactive kiosks and information displays with built-in charging

Future research directions should focus on expanding gesture vocabulary, increasing operational range, and developing adaptive models that learn user-specific gesture patterns over time.

6 References

1. Wang, W., Yang, L., Gan, L., & Xue, G. (2025). The Wireless Charger as a Gesture Sensor: A Novel Approach to Ubiquitous Interaction. In Proceedings of CHI Conference on Human Factors in Computing Systems.
2. National Highway Traffic Safety Administration. (2023). Distracted Driving Fatality Statistics.
3. Zhang et al. (2020). Privacy Concerns in Camera-Based Interaction Systems. ACM Computing Surveys.
4. MarketsandMarkets. (2024). Human-Machine Interface Market Global Forecast.
5. Zhu & Xie. (2019). CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. IEEE ICCV.
6. Statista. (2024). Global HMI Market Growth Projections.