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

TL;DR

This paper introduces a low-cost, high-precision human-machine interaction method using existing multi-coil wireless charging pads, enabling gesture recognition without additional sensors and outperforming traditional techniques.

Contribution

The paper presents a novel gesture recognition approach leveraging existing wireless charging pads' modules, eliminating extra sensors and improving accuracy and environmental adaptability.

Findings

Recognition accuracy improved by 0.73 points

Effective gesture identification across diverse scenarios

Low-cost solution utilizing existing hardware

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.