A Low-Cost, High-Precision Human-Machine Interaction Solution Based on Multi-Coil Wireless Charging Pads

TL;DR

The paper addresses the limitation of wireless charging pads by enabling low‑cost, high‑precision gesture-based human–machine interaction without wearables. It proposes a three‑part system consisting of an Energy Sensor Module, a Multi‑Coil Wireless Charging Module, and a Central Processing Unit that processes coil current/power data to recognize gestures using a Bayesian network for probabilistic reasoning and a particle filter for trajectory inference, with data preprocessing including high‑pass filtering and data slicing. Experimental results show a Bayesian‑network reasoning accuracy of $73\%$ and an overall improvement in recognition performance over baselines such as SVM, MLP, and CNN, demonstrating strong accuracy and adaptability across environments. The approach offers a practical, low‑cost path to broaden the utility of wireless charging pads in smart homes, offices, and public spaces, potentially accelerating the adoption of multifunctional charging surfaces.

Abstract

Wireless charging pads are common, yet their functionality is mainly restricted to charging. Existing gesture recognition techniques, such as those based on machine vision and WiFi, have drawbacks like high costs and poor precision. This paper presents a new human machine interaction solution using multicoil wireless charging pads. The proposed approach leverages the pads existing modules without additional wearable sensors. It determines gestures by monitoring current and power changes in different coils. The data processing includes noise removal, sorting, highpass filtering, and slicing. A Bayesian network and particle filtering are employed for motion tracking. Through experiments, this solution proves to have wide applications, high recognition accuracy, and low cost. It can effectively identify diverse gestures, increasing the value of wireless charging pads. It outperforms traditional methods, with a 0.73 improvement in recognition accuracy and better environmental adaptability.

Figures (10)

• Figure 1: System Workflow Diagram
• Figure 2: Workflow of Data Processing and Gesture Recognition in the Central Processing Unit
• Figure 3: Energy Sensor Module Interfaced with Multi-Coil Wireless Charging Pad
• Figure 4: Multi-Coil Wireless Charging Module with Integrated Sensors
• Figure 5: High-pass filtering and data segmentation