Gesture recognition allows distinguishing specific user motions that intend to express a message. The recognized gestures can be used in various applications such as humancomputer interface (HCI), clinical practice including rehabilitation, and personal identification. We propose a method of recognizing upper-limb motion gestures for HCI using electronic textile sensors, which consist of a double-layered structure with complementary resistance characteristics. For gesture recognition, we apply dynamic time warping (DTW) as it exhibits a high performance with simple computations for dynamic signals. We verified the functional feasibility of the proposed method from the data of 10 subjects performing 6 HCI gestures. The gesture classification accuracy for all subjects was 85.4%, although each subject separately achieved a higher performance. In fact, six subjects achieved a perfect recognition performance (100% recognition accuracy); three subjects achieved an accuracy of 98.6%, and one achieved an accuracy of 97.2%.

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.