scholarly journals A New Spiking Convolutional Recurrent Neural Network (SCRNN) With Applications to Event-Based Hand Gesture Recognition

2020 ◽  
Vol 14 ◽  
Author(s):  
Yannan Xing ◽  
Gaetano Di Caterina ◽  
John Soraghan
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Recognition Accuracy ◽  
Sequence Data ◽  
Arbitrary Length ◽  
Temporal Correlations ◽  
High Efficient ◽  
Recurrent Architecture ◽  
Event Based

The combination of neuromorphic visual sensors and spiking neural network offers a high efficient bio-inspired solution to real-world applications. However, processing event- based sequences remains challenging because of the nature of their asynchronism and sparsity behavior. In this paper, a novel spiking convolutional recurrent neural network (SCRNN) architecture that takes advantage of both convolution operation and recurrent connectivity to maintain the spatial and temporal relations from event-based sequence data are presented. The use of recurrent architecture enables the network to have a sampling window with an arbitrary length, allowing the network to exploit temporal correlations between event collections. Rather than standard ANN to SNN conversion techniques, the network utilizes a supervised Spike Layer Error Reassignment (SLAYER) training mechanism that allows the network to adapt to neuromorphic (event-based) data directly. The network structure is validated on the DVS gesture dataset and achieves a 10 class gesture recognition accuracy of 96.59% and an 11 class gesture recognition accuracy of 90.28%.

scholarly journals Consecrate Recurrent Neural Network Classifier for Autism Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 2033-2041
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Sequence Data ◽  
Large Data ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neural Network Classifier ◽  
Gene Sequences

Most recent discoveries in Autism Spectrum Disorder (ASD) detection and classification studies reveal that there is a substantial relationship between Autism disorders and gene sequences. This work is indented to classify the autism spectrum disorder groups and sub-groups based on the gene sequences. The gene sequences are large data and perplexed for handling with conventional data mining or classification procedures. The Consecrate Recurrent Neural Network Classifier for Autism Classification (CRNNC-AC) work is introduced in this work to classify autism disorders using gene sequence data. A dedicated Elman [1] type Recurrent Neural Network (RNN) is introduced along with a legacy Long Short-Term Memory (LSTM) [2] in this classifier. The LSTM model is contrived to achieve memory optimization to eliminate memory overflows without affecting the classification accuracy. The classification quality metrics [3] such as Accuracy, Sensitivity, Specificity and F1-Score are concerned for optimization. The processing time of the proposed method is also measured to evaluate the pertinency.

scholarly journals Fast identification method for express end sorting label code based on convolutional recurrent neural network

2020 ◽  
Vol 14 (8) ◽  
pp. 1689-1697
Author(s):  
Haiyan Du ◽  
Chunxue Wu ◽  
Yan Wu ◽  
Ren Han ◽  
Xiao Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Code Region ◽  
Digital Information ◽  
Coding Region ◽  
Recognition Method ◽  
Sequence Recognition ◽  
Sorting Process

Abstract In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.

scholarly journals Ambient Light Based Hand Gesture Recognition Enabled by Recurrent Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 7303-7312 ◽  
Author(s):  
Haihan Duan ◽  
Miao Huang ◽  
Yanbing Yang ◽  
Jie Hao ◽  
Liangyin Chen
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Ambient Light

scholarly journals A Recurrent Neural Network for Attenuating Non-cognitive Components of Pupil Dynamics

2021 ◽  
Vol 12 ◽  
Author(s):  
Sharath Koorathota ◽  
Kaveri Thakoor ◽  
Linbi Hong ◽  
Yaoli Mao ◽  
Patrick Adelman ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Sequence Data ◽  
Learning Sequence ◽  
Cognitive Components ◽  
Related Information ◽  
Deep Recurrent Neural Network ◽  
Model Predictions ◽  
Brain States ◽  
And Performance

There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal.

Multi-Sensor Guided Hand Gesture Recognition for a Teleoperated Robot Using a Recurrent Neural Network

2021 ◽  
Vol 6 (3) ◽  
pp. 6039-6045
Author(s):  
Wen Qi ◽  
Salih Ertug Ovur ◽  
Zhijun Li ◽  
Aldo Marzullo ◽  
Rong Song
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Hand Gesture Recognition ◽  
Hand Gesture

scholarly journals Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2106 ◽  
Author(s):  
Linchu Yang ◽  
Ji’an Chen ◽  
Weihang Zhu
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Leap Motion ◽  
Motion Controller ◽  
Hand Gestures ◽  
Leap Motion Controller ◽  
Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition is one of the most significant tools for human–computer interaction. In order to improve the accuracy of the dynamic hand gesture recognition, in this paper, a two-layer Bidirectional Recurrent Neural Network for the recognition of dynamic hand gestures from a Leap Motion Controller (LMC) is proposed. In addition, based on LMC, an efficient way to capture the dynamic hand gestures is identified. Dynamic hand gestures are represented by sets of feature vectors from the LMC. The proposed system has been tested on the American Sign Language (ASL) datasets with 360 samples and 480 samples, and the Handicraft-Gesture dataset, respectively. On the ASL dataset with 360 samples, the system achieves accuracies of 100% and 96.3% on the training and testing sets. On the ASL dataset with 480 samples, the system achieves accuracies of 100% and 95.2%. On the Handicraft-Gesture dataset, the system achieves accuracies of 100% and 96.7%. In addition, 5-fold, 10-fold, and Leave-One-Out cross-validation are performed on these datasets. The accuracies are 93.33%, 94.1%, and 98.33% (360 samples), 93.75%, 93.5%, and 98.13% (480 samples), and 88.66%, 90%, and 92% on ASL and Handicraft-Gesture datasets, respectively. The developed system demonstrates similar or better performance compared to other approaches in the literature.

scholarly journals Gesture recognition system based on CNN-IndRNN and OpenBCI

2021 ◽  
Vol 336 ◽  
pp. 06003
Author(s):  
Na Wu ◽  
Hao JIN ◽  
Xiachuan Pei ◽  
Shurong Dong ◽  
Jikui Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition System ◽  
Classification Performance ◽  
Non Invasive ◽  
Average Accuracy ◽  
Open Source Hardware ◽  
Semg Signals

Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.

scholarly journals Low Latency Based Convolutional Recurrent Neural Network Model for Speech Command Recognition

2021 ◽  
Vol 50 (4) ◽  
pp. 656-673
Author(s):  
Chhayarani Ram Kinkar ◽  
Yogendra Kumar Jain
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Resource Constraints ◽  
Recognition Accuracy ◽  
Limited Resources ◽  
Low Latency ◽  
Model Parameters ◽  
Memory Requirement ◽  
Proposed Model

The presented paper proposes a new speech command recognition model for novel engineering applications with limited resources. We built the proposed model with the help of a Convolutional Recurrent Neural Network (CRNN). The use of CRNN instead of Convolutional Neural Network (CNN) helps us to reduce the model parameters and memory requirement as per resource constraints. Furthermore, we insert transmute and curtailment layer between the layers of CRNN. By doing this we further reduce model parameters and float number of operations to half of the CRNN requirement. The proposed model is tested on Google’s speech command dataset. The obtained result shows that the proposed CRNN model requires 1/3 parameters as compared to the CNN model. The number of parameters of the CRNN model is further reduced by 45% and the float numbers of operations between 2% to 12 % in different recognition tasks. The recognition accuracy of the proposed model is 96% on Google’s speech command dataset, and on laboratory recording, its recognition accuracy is 89%.

scholarly journals Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhonghua Xia ◽  
Jinming Xing ◽  
Changzai Wang ◽  
Xiaofeng Li
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Time Domain ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Recognition Time ◽  
Overlap Ratio

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.

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