Real-time Interface Control with Motion Gesture Recognition based on Non-contact Capacitive Sensing

TL;DR

This paper presents a real-time non-contact gesture recognition system using capacitive sensing and a GRU model, achieving high accuracy and robustness for intuitive human-machine interaction.

Contribution

It introduces a novel framework that processes raw capacitive signals with adaptive thresholding and deep learning for accurate gesture classification in real-time.

Findings

Achieved 98.8% detection rate for gesture signals

Classified 10 gesture types with 98.79% accuracy

Demonstrated feasibility of non-contact gesture-based interfaces

Abstract

Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, and contact sensing interface application such as human-computer interaction. However, as a non-contact proximity sensing scheme is easily affected by the disturbance of peripheral objects or surroundings, it requires considerable sensitive data processing than contact sensing, limiting the use of its further utilization. In this paper, we propose a real-time interface control framework based on non-contact hand motion gesture recognition through processing the raw signals, detecting the electric field disturbance triggered by the hand gesture movements near the capacitive sensor using adaptive threshold, and extracting the significant signal frame, covering the authentic signal intervals with 98.8% detection rate and 98.4% frame correction rate. Through the GRU model trained with the extracted signal frame, we classify the 10 hand motion gesture types with 98.79% accuracy. The framework transmits the classification result and maneuvers the interface of the foreground process depending on the input. This study suggests the feasibility of intuitive interface technology, which accommodates the flexible interaction between human to machine similar to Natural User Interface, and uplifts the possibility of commercialization based on measuring the electric field disturbance through non-contact proximity sensing which is state-of-the-art sensing technology.