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 ob...

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Bibliographic Details
Published inSensors and materials Vol. 32; no. 6; p. 2167
Main Authors Ahn, Eun-Ji, Han, Sang-Ho, Ryu, Mun-Ho, Kim, Je-Nam
Format Journal Article
LanguageEnglish
Published Tokyo MYU Scientific Publishing Division 30.06.2020
Subjects
Gesture recognition
Keyboards
Machine learning
Recognition
Sensors
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Summary: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.
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ISSN:0914-4935
DOI:10.18494/SAM.2020.2832