scholarly journals Correction to: Deep learning-based elderly gender classification using Doppler radar

2021 ◽  
Author(s):  
ZhiChen Wang ◽  
Zelin Meng ◽  
Kenshi Saho ◽  
Kazuki Uemura ◽  
Naoto Nojiri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Radar ◽  
Gender Classification

Deep learning-based elderly gender classification using Doppler radar

2021 ◽  
Author(s):  
ZhiChen Wang ◽  
Zelin Meng ◽  
Keshi Saho ◽  
Kazuki Uemura ◽  
Naoto Nojiri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Radar ◽  
Gender Classification

scholarly journals Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3937
Author(s):  
Seungeon Song ◽  
Bongseok Kim ◽  
Sangdong Kim ◽  
Jonghun Lee
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Smart Home ◽  
Doppler Radar ◽  
Radar Images ◽  
Radar Signature ◽  
Memory Efficiency ◽  
High Compression ◽  
Value Decomposition ◽  
Deep Learning Model

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

scholarly journals Gender Classification System Based on Deep Learning in Low Power Embedded Board

2017 ◽  
Vol 6 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Hyunwook Jeong ◽  
Dae Hoe Kim ◽  
Wisam J. Baddar ◽  
Yong Man Ro
Keyword(s):  
Deep Learning ◽  
Low Power ◽  
Classification System ◽  
Gender Classification

Sign Language Gesture Recognition Using Doppler Radar and Deep Learning

2019 ◽  
Author(s):  
Hovannes Kulhandjian ◽  
Prakshi Sharma ◽  
Michel Kulhandjian ◽  
Claude D'Amours
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Gesture Recognition ◽  
Doppler Radar

Age and Gender Classification Based on Deep Learning

2021 ◽  
pp. 425-437
Author(s):  
Tejas Agarwal ◽  
Mira Andhale ◽  
Anand Khule ◽  
Rushikesh Borse
Keyword(s):  
Deep Learning ◽  
Gender Classification ◽  
Age And Gender ◽  
And Gender

3D Fingerprint Gender Classification Using Deep Learning

2019 ◽  
pp. 37-45 ◽  
Author(s):  
Haozhe Liu ◽  
Wentian Zhang ◽  
Feng Liu ◽  
Yong Qi
Keyword(s):  
Deep Learning ◽  
Gender Classification
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