Name
Abstract | ||
|---|---|---|
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 aims to propose a method for classifying hand gestures by creating sound in the non-audible 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICUFN.2019.8806145 | 2019 ELEVENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2019) |
Keywords | Field | DocType |
non-audible sound, hand gesture, gesture classification, convolutional neural network, shoat-time fourier transform | Pattern recognition,Convolutional neural network,Computer science,Gesture,Short-time Fourier transform,Fourier transform,Artificial intelligence,Statistical classification,Wearable technology,Smartwatch,Distributed computing,Gesture classification | Conference |
ISSN | Citations | PageRank |
2165-8528 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinhyuck Kim | 1 | 0 | 0.34 |
| Jinwon Cheon | 2 | 0 | 0.34 |
| Sunwoong Choi | 3 | 112 | 15.89 |