Virtual Keyboard Recognition with e-Textile Sensors
Eun-Ji Ahn, Sang-Ho Han, Mun-Ho Ryu, Je-Nam Kim · Sensors and Materials · 2020
In this study, we propose a gesture recognition method using e-textile sensors and involving the pressing of numeric keys from "0" to "9".An e-textile sensor comprises a double-layer structure with complementary resistance characteristics, and it is attached to the garment to obtain a resistance signal.For gesture recognition, we tested dynamic time warping (DTW), machine learning, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM).Before applying each machine learning technique, we performed normalization and resized the data to ensure that they are of the same length.A total of 120 iterations were performed for each gesture for a single subject.The results indicate that the lowest gesture classification accuracy for DTW was 74.2%, followed by 78.8 and 91.6% for LSTM and BiLSTM, respectively.