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Hand Gesture Classification Based on Nonaudible Sound Using Convolutional Neural Network
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-18
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Recognizing and distinguishing the behavior and gesture of a user has become important owing to an increase in the use of wearable devices, such as a smartwatch.
This study is aimed at proposing a method for classifying hand gestures by creating sound in the nonaudible frequency range using a smartphone and reflected signal.
The proposed method converts the sound data, which has been reflected and recorded, into an image within a short time using short-time Fourier transform, and the obtained data are applied to a convolutional neural network (CNN) model to classify hand gestures.
The results showed classification accuracy for 8 hand gestures with an average of 87.75%.
Additionally, it is confirmed that the suggested method has a higher classification accuracy than other machine learning classification algorithms.
American Psychological Association (APA)
Kim, Jinhyuck& Choi, Sunwoong. 2019. Hand Gesture Classification Based on Nonaudible Sound Using Convolutional Neural Network. Journal of Sensors،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1187170
Modern Language Association (MLA)
Kim, Jinhyuck& Choi, Sunwoong. Hand Gesture Classification Based on Nonaudible Sound Using Convolutional Neural Network. Journal of Sensors No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1187170
American Medical Association (AMA)
Kim, Jinhyuck& Choi, Sunwoong. Hand Gesture Classification Based on Nonaudible Sound Using Convolutional Neural Network. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1187170
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187170