Sign Gesture Recognition from Raw Skeleton Information in 3D Using Deep Learning

2021 ◽  
pp. 184-195
Author(s):  
Sumit Rakesh ◽  
Saleha Javed ◽  
Rajkumar Saini ◽  
Marcus Liwicki
Keyword(s):  
Deep Learning ◽  
Gesture Recognition

Deep-Learning for Hand-Gesture Recognition with Simultaneous Thermal and Radar Sensors

2020 ◽  
Author(s):  
Sruthy Skaria ◽  
Da Huang ◽  
Akram Al-Hourani ◽  
Robin J. Evans ◽  
Margaret Lech
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Radar Sensors

scholarly journals Intersected EMG Heatmaps and Deep Learning Based Gesture Recognition

2020 ◽  
Author(s):  
Weijie Ke ◽  
Yannan Xing ◽  
Gaetano Di Caterina ◽  
Lykourgos Petropoulakis ◽  
John Soraghan
Keyword(s):  
Deep Learning ◽  
Gesture Recognition

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 Analysis of Edge-Optimized Deep Learning Classifiers for Radar-based Gesture Recognition

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mateusz Chmurski ◽  
Mariusz Zubert ◽  
Kay Bierzynski ◽  
Avik Santra
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Learning Classifiers

deepGesture: Deep learning-based gesture recognition scheme using motion sensors

Displays ◽  
2018 ◽  
Vol 55 ◽  
pp. 38-45 ◽  
Author(s):  
Ji-Hae Kim ◽  
Gwang-Soo Hong ◽  
Byung-Gyu Kim ◽  
Debi P. Dogra
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Motion Sensors
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