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

arXiv: https://arxiv.org/abs/2405.17038 · HTML: https://mlsanity.com/paper/2405.17038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 4 of 4)

- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition](https://mlsanity.com/paper/2110.12396/) (2021)
- [Active Visuo-Tactile Interactive Robotic Perception for Accurate Object Pose Estimation in Dense Clutter](https://mlsanity.com/paper/2202.02207/) (2022)
- [Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling](https://mlsanity.com/paper/1802.09901/) (2018)

## Abstract (arXiv)

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.