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On the Effect of Training Convolution Neural Network for Millimeter-Wave Radar-Based Hand Gesture Recognition
2021
Sensors
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The purpose of this paper was to investigate the effect of a training state-of-the-art convolution neural network (CNN) for millimeter-wave radar-based hand gesture recognition (MR-HGR). ...
Meanwhile, for the different data modality in MR-HGR, a parameterized representation of temporal space-velocity (TSV) spectrogram was proposed as an integrated data modality of the time-evolving hand gesture ...
Acknowledgments: The authors give their acknowledgments to the participants in the experiments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21010259
pmid:33401744
fatcat:zk35wm7a3nfuphl62pssnv5i7i
Gesture Recognition Using LFMCW Radar and Convolutional Neural Network
2019
DEStech Transactions on Computer Science and Engineering
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In order to overcome the limitations of traditional visible optical sensors, such as low-light, occlusion and privacy, a gesture recognition system based on linear frequency modulation continuous wave ...
The R-D map series is taken as the three dimensional convolutional neural network (3D-CNN) input samples, and the gesture features are extracted via convolution layer and pooling layer, and the gesture ...
Acknowledgement The authors give thanks to Minglang Qiao for offering the CNN code. ...
doi:10.12783/dtcse/cscme2019/32550
fatcat:7a5ljyjtvvdz5lez3u6qna3gva
Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM
2022
Frontiers in Neurorobotics
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The Soli dataset is based on the dynamic gesture signals collected by millimeter-wave radar. ...
In this article, a multi-layer convolutional neural network (ResNet-18) and Long Short-Term Memory Networks (LSTM) model is proposed for dynamic gesture recognition. ...
The algorithms of deep learning in radar gesture recognition mainly include Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). ...
doi:10.3389/fnbot.2022.903197
fatcat:twvxjxcuyrh3hflkczxethm5ny
Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing
2018
IEEE Access
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2017 pre-print arXiv:1712.00216v1 fatcat:rr6wp3td4zcrxfrryvnbwtnrbiOther Versions
The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition by machine learning. ...
Furthermore, to achieve higher classification accuracy, we utilize an end-to-end neural network model and obtain a recognition accuracy of 96.32\%. ...
ACKNOWLEDGMENT We are grateful to all the participants for their efforts in collecting hand gesture data for our experiments. ...
doi:10.1109/access.2018.2868268
fatcat:5pdgpx24rzewvjv6zjz4dzjiai
Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks Using Novel Sterile Training Technique
2021
IEEE Access
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INDEX TERMS Convolutional neural networks, deep learning, hand gesture recognition, millimeter-wave radar, sterile training. ...
In this paper, we investigate novel data collection and training techniques towards improving classification accuracy of non-moving (static) hand gestures using a convolutional neural network (CNN) and ...
NETWORK ARCHITECTURE AND TRAINING The networks used to classify the hand gesture vary based on the preprocessing applied to the dataset. ...
doi:10.1109/access.2021.3051454
fatcat:apwphiipcvbudnty5zljxoqzca
Long-Range Gesture Recognition Using Millimeter Wave Radar
[article]
2020
arXiv
pre-print
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Millimeter wave (mmWave) based gesture recognition technology provides a good human computer interaction (HCI) experience. ...
In this paper, we design a long-range gesture recognition model which utilizes a novel data processing method and a customized artificial Convolutional Neural Network (CNN). ...
At the same time, it also has the advantage of avoiding the interference of other electromagnetic waves. For example, LiGest [6] , is a hand gesture recognition model based on visible light. ...
arXiv:2002.02591v1
fatcat:lgjjom5t7feofnompjrtzdjnne
Activity Recognition Based on Millimeter-Wave Radar by Fusing Point Cloud and Range–Doppler Information
2022
Signals
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, and high-knee lifting, based on a millimeter-wave radar. ...
Then we fuse the two features and input the fused feature into the full connected layer for classification. We built a dataset based on a 3D millimeter-wave radar from 17 volunteers. ...
Acknowledgments: In the process of research, we attained the help of Qinzhe Li (College of electronic and information engineering, Tongji University) and Junyan Ge (College of Computer Science, Jiangsu ...
doi:10.3390/signals3020017
fatcat:nrelwuklyraknmyhsiqnyuhlza
Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation
2020
Electronics
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However, the main obstacle in the commercialization of radar-based hand gesture recognition is that even for the same type of hand gesture, recognition accuracy is degraded due to a slight difference in ...
To verify the effectiveness of domain adaptation, a domain discriminator that cheats the classifier was applied to a deep learning network with a convolutional neural network (CNN) structure. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics9122140
fatcat:cwxe7foifjatpnjnpzyuhxvyja
EM-Sign: A Non-Contact Recognition Method Based on 24 GHz Doppler Radar for Continuous Signs and Dialogues
2020
Electronics
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Our work provides an alternative form of sign language recognition and a new approach to simplify the training process and achieve a better continuous sign language recognition effect. ...
We studied continuous sign language recognition using Doppler radar sensors. ...
Acknowledgments: The authors acknowledge the participants in the experiments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics9101577
fatcat:apotncey3jgazefre4y57zkexm
Enhanced Multi-channel Feature Synthesis for Hand Gesture Recognition Based on CNN with a Channel and Spatial Attention Mechanism
2020
IEEE Access
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Millimeter-wave (MMW) radar hand gesture recognition technology is becoming important in many electronic device control applications. ...
The algorithm blends the information of both micro-Doppler features and instantaneous angles (azimuth and elevation) to accomplish hand gesture recognition performed with the convolutional neural network ...
ACKNOWLEDGMENT The authors would like to thank the editor and reviewers for their constructive comments and suggestions, which improve the quality of this paper. ...
doi:10.1109/access.2020.3010063
fatcat:ticlvryomfgkvot3z5n2mdyori
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal
[article]
2021
arXiv
pre-print
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Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. ...
Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interface. ...
In the past years, gesture recognition based on different wireless mediums, including WiFi [30] [31] [32] [33] [34] [35] , acoustic signals [36] and millimeter wave [37] has been investigated. ...
arXiv:2111.06195v1
fatcat:gj3r4bq5vfefdiwkipmyemugpa
GestureVLAD: Combining Unsupervised Features Representation and Spatio-Temporal Aggregation for Doppler-Radar Gesture Recognition
2019
IEEE Access
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INDEX TERMS Convolutional neural networks, Doppler-radar, feature aggregation, hand gesture recognition, unsupervised representation learning. ...
To overcome the challenges in the recognition task and the limitations of the current methods, we propose a shallow learning approach for gesture recognition, that is based on unsupervised range-Doppler ...
For such methods, Convolutional Neural Networks (CNNs) are trained on the input Doppler-radar [3] , [17] , [18] . ...
doi:10.1109/access.2019.2942305
fatcat:x37fqsbpmzdujdbrhmdtx5nrta
Radar-Based Hand Gesture Recognition Using Spiking Neural Networks
2021
Electronics
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We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. ...
The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics10121405
fatcat:vasb26pc7zdpzj23cqdx6f5zzy
A Movement Detection System Using Continuous-Wave Doppler Radar Sensor and Convolutional Neural Network to Detect Cough and Other Gestures
2020
IEEE Sensors Journal
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Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. ...
Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. ...
Convolutional Neural Network Convolutional neural network (ConvNets or CNNs) is a type of Deep Learning being one of the main types to do images recognition and image classifications. ...
doi:10.1109/jsen.2020.3028494
fatcat:a33fmjgnwjgvziimz7syur72la
Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability
2021
Mobile Information Systems
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Hand gesture recognition is one of the most sought technologies in the field of machine learning and computer vision. ...
There has been an unprecedented demand for applications through which one can detect the hand signs for deaf people and people who use sign language to communicate, thereby detecting hand signs and correspondingly ...
Wernersson, “Pulsed millimeter wave radar for hand
neural networks for remote sensing image classification,” IET gesture sensing and classification,” IEEE Sensors Letters, vol. 3,
Computer ...
doi:10.1155/2021/4912486
fatcat:d6wsy6wfhfgyjc6crb66qh6ah4
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