| Autor: |
Sruthy Skaria, Akram Al-Hourani, Robin J. Evans |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 8, Pp 203580-203590 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2020.3037062 |
| Popis: |
Using deep-learning techniques for analyzing radar signatures has opened new possibilities in the field of smart-sensing, especially in the applications of hand-gesture recognition. In this paper, we present a framework, using deep-learning techniques, to classify hand-gesture signatures generated from an ultra-wideband (UWB) impulse radar. We extract the signals of 14 different hand-gestures and represent each signature as a 3-dimensional tensor consisting of range-Doppler frame sequence. These signatures are passed to a convolutional neural network (CNN) to extract the unique features of each gesture, and are then fed to a classifier. We compare 4 different classification architectures to predict the gesture class, namely; (i) fully connected neural network (FCNN), (ii) k-Nearest Neighbours (k-NN), (iii) support vector machine (SVM), (iv) long short term memory (LSTM) network. The shape of the range-Doppler-frame tensor and the parameters of the classifiers are optimized in order to maximize the classification accuracy. The classification results of the proposed architectures show a high level of accuracy above 96 % and a very low confusion probability even between similar gestures. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
