分散法線力とせん断力を用いた触覚ジェスチャの深層学習分類
Deep Learning Classification of Touch Gestures Using Distributed Normal and Shear Force
柔軟な三軸触覚センサアレイで13種類の触覚ジェスチャデータを収集し、CNNで時空間特徴を学習。せん断力データを加えることで認識精度が66%から74%に向上した。
著者: Hojung Choi, Dane Brouwer, Michael A. Lin, Kyle T. Yoshida, Carine Rognon, Benjamin Stephens-Fripp, Allison M. Okamura, Mark R. Cutkosky
分類: cs.RO, cs.HC
原文アブストラクト
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.
