论文标题
使用分布式正常和剪切力对触摸手势的深度学习分类
Deep Learning Classification of Touch Gestures Using Distributed Normal and Shear Force
论文作者
论文摘要
当人类通过触摸与另一个代理（例如人，宠物或机器人）进行社交互动时，他们通过使用不同的方向，位置，接触区域和持续时间施加不同量的力来做到这一点。虽然先前的触摸手势识别工作集中在正常力的时空分布上，但我们假设增加剪切力将允许更可靠的分类。我们为一个人或机器人的手臂提供了柔软，柔软的皮肤，并带有一系列三轴触觉传感器。我们使用它通过用户研究来收集13个触摸手势类别的数据，并训练卷积神经网络（CNN）从记录的数据中学习时空特征。通过正常和剪切数据，该网络获得了74％的识别精度，而仅使用正常力数据获得了66％的识别精度。在13个触摸手势类中，添加分布式剪切数据提高了分类精度。
When humans socially interact with another agent (e.g., human, pet, or robot) through touch, they do so by applying varying amounts of force with different directions, locations, contact areas, and durations. While previous work on touch gesture recognition has focused on the spatio-temporal distribution of normal forces, we hypothesize that the addition of shear forces will permit more reliable classification. We present a soft, flexible skin with an array of tri-axial tactile sensors for the arm of a person or robot. We use it to collect data on 13 touch gesture classes through user studies and train a Convolutional Neural Network (CNN) to learn spatio-temporal features from the recorded data. The network achieved a recognition accuracy of 74% with normal and shear data, compared to 66% using only normal force data. Adding distributed shear data improved classification accuracy for 11 out of 13 touch gesture classes.
