Sequentially Supervised Long Short-Term Memory for Gesture Recognition

2016 ◽  
Vol 8 (5) ◽  
pp. 982-991 ◽  
Author(s):  
Peisong Wang ◽  
Qiang Song ◽  
Hua Han ◽  
Jian Cheng
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6824
Author(s):  
Jin-Su Kim ◽  
Min-Gu Kim ◽  
Sung-Bum Pan
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Emg Signal ◽  
Long Short Term Memory ◽  
Lstm Network

Electromyogram (EMG) signals cannot be forged and have the advantage of being able to change the registered data as they are characterized by the waveform, which varies depending on the gesture. In this paper, a two-step biometrics method was proposed using EMG signals based on a convolutional neural network–long short-term memory (CNN-LSTM) network. After preprocessing of the EMG signals, the time domain features and LSTM network were used to examine whether the gesture matched, and single biometrics was performed if the gesture matched. In single biometrics, EMG signals were converted into a two-dimensional spectrogram, and training and classification were performed through the CNN-LSTM network. Data fusion of the gesture recognition and single biometrics was performed in the form of an AND. The experiment used Ninapro EMG signal data as the proposed two-step biometrics method, and the results showed 83.91% gesture recognition performance and 99.17% single biometrics performance. In addition, the false acceptance rate (FAR) was observed to have been reduced by 64.7% through data fusion.

Deep neural learning techniques with long short-term memory for gesture recognition

2020 ◽  
Vol 32 (20) ◽  
pp. 16073-16089
Author(s):  
Deepak Kumar Jain ◽  
Aniket Mahanti ◽  
Pourya Shamsolmoali ◽  
Ramachandran Manikandan
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Neural Learning ◽  
Learning Techniques ◽  
Long Short Term Memory

scholarly journals Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

2020 ◽  
Author(s):  
Doreen Jirak ◽  
Stephan Tietz ◽  
Hassan Ali ◽  
Stefan Wermter
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Gesture Recognition ◽  
Network Architecture ◽  
Short Term Memory ◽  
Medical Rehabilitation ◽  
Measurement Unit ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Recent developments of sensors that allow tracking of human movements and gestures enable rapid progress of applications in domains like medical rehabilitation or robotic control. Especially the inertial measurement unit (IMU) is an excellent device for real-time scenarios as it rapidly delivers data input. Therefore, a computational model must be able to learn gesture sequences in a fast yet robust way. We recently introduced an echo state network (ESN) framework for continuous gesture recognition (Tietz et al., 2019) including novel approaches for gesture spotting, i.e., the automatic detection of the start and end phase of a gesture. Although our results showed good classification performance, we identified significant factors which also negatively impact the performance like subgestures and gesture variability. To address these issues, we include experiments with Long Short-Term Memory (LSTM) networks, which is a state-of-the-art model for sequence processing, to compare the obtained results with our framework and to evaluate their robustness regarding pitfalls in the recognition process. In this study, we analyze the two conceptually different approaches processing continuous, variable-length gesture sequences, which shows interesting results comparing the distinct gesture accomplishments. In addition, our results demonstrate that our ESN framework achieves comparably good performance as the LSTM network but has significantly lower training times. We conclude from the present work that ESNs are viable models for continuous gesture recognition delivering reasonable performance for applications requiring real-time performance as in robotic or rehabilitation tasks. From our discussion of this comparative study, we suggest prospective improvements on both the experimental and network architecture level.

scholarly journals Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

2020 ◽  
Vol 25 (1) ◽  
pp. 57-61
Author(s):  
Falah Obaid ◽  
Amin Babadi ◽  
Ahmad Yoosofan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Hand Gesture ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

AbstractDeep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.

A long short-term memory based Schaeffer gesture recognition system

2017 ◽  
Vol 35 (2) ◽  
pp. e12247 ◽  
Author(s):  
S. O. Oprea ◽  
A. Garcia-Garcia ◽  
S. Orts-Escolano ◽  
V. Villena-Martinez ◽  
J. A. Castro-Vargas
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Recognition System ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Accuracy electroencephalography classification by a regularized long short-term memory network

2021 ◽  
pp. 107754632110093
Author(s):  
Zhenying Gong ◽  
Tao Wang ◽  
Zhen Zhao ◽  
Xin Liu ◽  
Yina Guo ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Operating Experience ◽  
Computer Interface ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

The motor-based brain–computer interface is widely used in the exoskeleton reconstruction of patients with muscle weakness and to enhance the operating experience of somatosensory game customers through the combination of actions and electroencephalography signals. However, the recognition algorithms in traditional motor-based brain–computer interfaces have problems such as “brain–computer interface blindness” (recognition accuracy is less than 70%) and “one person one model.” In this study, a regularized long short-term memory algorithm and a hardware platform for gesture recognition by using the motor-based brain–computer interface are proposed. Experimental results show that the gesture recognition accuracy rate based on the motor brain–computer interface is up to 95.69%, which is significantly better than that of other algorithms. The proposed model enhances the applicability and generalization ability of the brain–computer interface, for which the practicability and effectiveness are verified.

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