Virtual Keyboard Recognition with e-Textile Sensors

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.