Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction

TL;DR

This paper presents a comprehensive evaluation of tactile hand gesture recognition methods using conductive textile interfaces, advancing real-time, robust HMI systems by analyzing traditional and deep learning approaches.

Contribution

It provides a detailed comparison of gesture recognition techniques on tactile textiles, highlighting the effectiveness of deep learning for real-time, variation-tolerant HMI applications.

Findings

Deep learning techniques outperform traditional methods in accuracy.

The system accommodates variations in hand size, pressure, and movement.

Real-time gesture interpretation is feasible with the proposed approaches.

Abstract

Motivated by the growing interest in enhancing intuitive physical Human-Machine Interaction (HRI/HVI), this study aims to propose a robust tactile hand gesture recognition system. We performed a comprehensive evaluation of different hand gesture recognition approaches for a large area tactile sensing interface (touch interface) constructed from conductive textiles. Our evaluation encompassed traditional feature engineering methods, as well as contemporary deep learning techniques capable of real-time interpretation of a range of hand gestures, accommodating variations in hand sizes, movement velocities, applied pressure levels, and interaction points. Our extensive analysis of the various methods makes a significant contribution to tactile-based gesture recognition in the field of human-machine interaction.