Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm

• Main Authors: Li, M. (Author), Lv, S. (Author), Song, Y. (Author), Wang, F. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Brain; contact pattern; Contact pattern; Convolution; Convolutional neural network; convolutional neural network (CNN); Convolutional neural networks; Decision trees; flexible tactile sensor; Flexible tactile sensors; Fluorine compounds; Fusion algorithms; Long short-term memory; long short-term memory (LSTM) network; Long short-term memory network; Memory network; Pattern recognition; Random forest algorithm; recognition; Recognition; Tactile sensors
• Online Access: View Fulltext in Publisher

 Recognizing different contact patterns imposed on tactile sensors plays a very important role in human–machine interaction. In this paper, a flexible tactile sensor with great dynamic response characteristics is designed and manufactured based on polyvinylidene fluoride (PVDF) ma-terial. Four contact patterns (stroking, patting, kneading, and scratching) are applied to the tactile sensor, and time sequence data of the four contact patterns are collected. After that, a fusion model based on the convolutional neural network (CNN) and the long-short term memory (LSTM) neural network named CNN-LSTM is constructed. It is used to classify and recognize the four contact patterns loaded on the tactile sensor, and the recognition accuracies of the four patterns are 99.60%, 99.67%, 99.07%, and 99.40%, respectively. At last, a CNN model and a random forest (RF) algorithm model are constructed to recognize the four contact patterns based on the same dataset as those for the CNN-LSTM model. The average accuracies of the four contact patterns based on the CNN-LSTM, the CNN, and the RF algorithm are 99.43%, 96.67%, and 91.39%, respectively. All of the experimental results indicate that the CNN-LSTM constructed in this paper has very efficient performance in recognizing and classifying the contact patterns for the flexible tactile sensor. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.