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.

本研究旨在提出一个强大的触觉手势识别系统，通过对各种手势识别方法进行综合评估，包括传统的特征工程方法和能够实时解释各种手势的当代深度学习技术，以适应手的大小、运动速度、施加压力水平和交互点的变化，我们对各种方法的深入分析为人机交互领域的基于触觉的手势识别做出了重要贡献。