Classification method of tactile feeling using stacked autoencoder based on haptic primary colors
PubDate: April 2017
Teams: The University of Tokyo；Keio Gijuku Daigaku
Writers: Fumihiro Kato; Charith Lasantha Fernando; Yasuyuki Inoue; Susumu Tachi
We have developed a classification method of tactile feeling using a stacked autoencoder-based neural network on haptic primary colors. The haptic primary colors principle is a concept of decomposing the human sensation of tactile feeling into force, vibration, and temperature. Images were obtained from variation in the frequency of the time series of the tactile feeling obtained when tracing a surface of an object, features were extracted by employing a stacked autoencoder using a neural network with two hidden layers, and supervised learning was conducted. We confirmed that the tactile feeling for three different surface materials can be classified with an accuracy of 82.0 [%].