scholarly journals Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2140
Author(s):  
Hyo Ryun Lee ◽  
Jihun Park ◽  
Young-Joo Suh
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Electronic Device ◽  
Domain Adaptation ◽  
Recognition Accuracy ◽  
Hand Gesture Recognition ◽  
Training Data ◽  
Hand Gesture ◽  
60 Ghz ◽  
Fmcw Radar

With the recent development of small radars with high resolution, various human–computer interaction (HCI) applications using them have been developed. In particular, a method of applying a user’s hand gesture recognition using a short-range radar to an electronic device is being actively studied. In general, the time delay and Doppler shift characteristics that occur when a transmitted signal that is reflected off an object returns are classified through deep learning to recognize the motion. 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 movement for each individual user. To solve this problem, in this paper, the domain adaptation is applied to hand gesture recognition to minimize the differences among users’ gesture information in the learning and the use stage. 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. Seven different hand gesture data were collected for 10 participants and used for learning, and the hand gestures of 10 users that were not included in the training data were input to confirm the recognition accuracy of an average of 98.8%.

Hand gesture intention-based identity recognition using various recognition strategies incorporated with VGG convolution neural network-extracted deep learning features

2021 ◽  
pp. 1-14
Author(s):  
Ing-Jr Ding ◽  
Nai-Wei Zheng ◽  
Meng-Chuan Hsieh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Strategies ◽  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Identity Recognition ◽  
Human Behaviors ◽  
Deep Learning Features

With fast developments of artificial intelligence, human behaviors can be further acknowledged by means of the biometric information of hand gesture actions made by the person. Such hand gesture information revealing the specific intention of the person will be undoubtedly a critical clue to cognize human behaviors. Furthermore, identity recognition of the hand gesture-making person is one of the most important technique issues in hand gesture recognition applications. This work explores hand gesture intention-based identity recognition where various deep learning recognition strategies are presented. The well-know image sensor of Leap Motion Controller (LMC) is employed in this work for acquisitions of active hand gesture data. This paper presents four different deep learning strategies for hand gesture intention-based identity recognition, all of which are based on the deep learning model of the visual geometry group (VGG)-type convolution neural network (CNN). The presented deep learning strategies to perform hand gesture intention-based identity recognition are typical VGG-16 CNN deep learning, dynamic time warping (DTW) classifications with VGG-16 CNN extracted deep learning features, DTW classifications by VGG-16 CNN extracted deep learning features with principal component analysis (PCA) data reduction, and PCA centroid classifications using VGG-16 CNN extracted deep learning features with PCA. Compared with traditional hand gesture recognition by classifications of only the geometrical space feature of LMC 3D-(x, y, z) data without any deep learning, most of presented VGG-CNN based deep learning approaches have more outstanding performances on recognition accuracy. In the situation of real-time recognition that considers both of recognition accuracy and computation time, PCA centroid classifications by VGG-16 CNN extracted deep learning features with PCA reduction, FC1-PCA and FC2-PCA features that are estimated from the first and the second fully connected (FC) layer of VGG-CNN respectively (i.e. FC1 and FC2 layers) and then significantly reduced the data dimension by PCA, apparently performs best among all presented deep learning strategies.

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 Electromyogram-based hand gesture recognition robust to various arm postures

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Kiwon Rhee ◽  
Hyun-Chool Shin
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Practical Applications ◽  
Electrode Positioning ◽  
Recognition Technique

In the recognition of electromyogram-based hand gestures, the recognition accuracy may be degraded during the actual stage of practical applications for various reasons such as electrode positioning bias and different subjects. Besides these, the change in electromyogram signals due to different arm postures even for identical hand gestures is also an important issue. We propose an electromyogram-based hand gesture recognition technique robust to diverse arm postures. The proposed method uses both the signals of the accelerometer and electromyogram simultaneously to recognize correct hand gestures even for various arm postures. For the recognition of hand gestures, the electromyogram signals are statistically modeled considering the arm postures. In the experiments, we compared the cases that took into account the arm postures with the cases that disregarded the arm postures for the recognition of hand gestures. In the cases in which varied arm postures were disregarded, the recognition accuracy for correct hand gestures was 54.1%, whereas the cases using the method proposed in this study showed an 85.7% average recognition accuracy for hand gestures, an improvement of more than 31.6%. In this study, accelerometer and electromyogram signals were used simultaneously, which compensated the effect of different arm postures on the electromyogram signals and therefore improved the recognition accuracy of hand gestures.

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