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.

scholarly journals Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 672 ◽  
Author(s):  
Lin Chen ◽  
Jianting Fu ◽  
Yuheng Wu ◽  
Haochen Li ◽  
Bin Zheng
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Surface Electromyography ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Hand Gesture ◽  
Proposed Model ◽  
Electromyography Signals ◽  
Motion Intention ◽  
Semg Signals

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

scholarly journals Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes

2020 ◽  
Vol 10 (18) ◽  
pp. 6507
Author(s):  
Yen-Cheng Chu ◽  
Yun-Jie Jhang ◽  
Tsung-Ming Tai ◽  
Wen-Jyi Hwang
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Prototype System ◽  
Recognition Time ◽  
Sensory Data ◽  
High Classification Accuracy ◽  
Different Types

The objective of this study is to present novel neural network (NN) algorithms and systems for sensor-based hand gesture recognition. The algorithms are able to classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. They are the extensions from the PairNet, which is a Convolutional Neural Network (CNN) capable of carrying out simple pairing operations with low computational complexities. Three different types of feedforward NNs, termed Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are proposed for the extension. They are the PairNet operating in conjunction with short-cut connections and/or inception modules for achieving high classification accuracy and low computation complexity. A prototype system based on smart phones for remote control of home appliances has been implemented for the performance evaluation. Experimental results reveal that the PairNet has superior classification accuracy over its basic CNN and Recurrent NN (RNN) counterparts. Furthermore, the Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are able to further improve classification hit rate and/or reduce recognition time for hand gesture recognition.

scholarly journals A Novel Image Classification Approach via Dense-MobileNet Models

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Wei Wang ◽  
Yutao Li ◽  
Ting Zou ◽  
Xin Wang ◽  
Jieyu You ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Feature Maps ◽  
Computation Cost ◽  
Classification Approach ◽  
Designed Experiments ◽  
Input Feature ◽  
Small Growth

As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

scholarly journals Social Touch Gesture Recognition Using Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Saad Albawi ◽  
Oguz Bayat ◽  
Saad Al-Azawi ◽  
Osman N. Ucan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Recognition System ◽  
Human Robot Interaction ◽  
Sensor Data ◽  
Social Touch ◽  
Minimum Number ◽  
Original Frame

Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.

scholarly journals Deep Neural Network Model of Hearing-Impaired Speech-in-Noise Perception

2020 ◽  
Vol 14 ◽  
Author(s):  
Stephanie Haro ◽  
Christopher J. Smalt ◽  
Gregory A. Ciccarelli ◽  
Thomas F. Quatieri
Keyword(s):  
Neural Network ◽  
Speech Perception ◽  
Background Noise ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Auditory Function ◽  
Human Speech ◽  
Digit Recognition ◽  
Speech In Noise ◽  
Hearing Damage

Many individuals struggle to understand speech in listening scenarios that include reverberation and background noise. An individual's ability to understand speech arises from a combination of peripheral auditory function, central auditory function, and general cognitive abilities. The interaction of these factors complicates the prescription of treatment or therapy to improve hearing function. Damage to the auditory periphery can be studied in animals; however, this method alone is not enough to understand the impact of hearing loss on speech perception. Computational auditory models bridge the gap between animal studies and human speech perception. Perturbations to the modeled auditory systems can permit mechanism-based investigations into observed human behavior. In this study, we propose a computational model that accounts for the complex interactions between different hearing damage mechanisms and simulates human speech-in-noise perception. The model performs a digit classification task as a human would, with only acoustic sound pressure as input. Thus, we can use the model's performance as a proxy for human performance. This two-stage model consists of a biophysical cochlear-nerve spike generator followed by a deep neural network (DNN) classifier. We hypothesize that sudden damage to the periphery affects speech perception and that central nervous system adaptation over time may compensate for peripheral hearing damage. Our model achieved human-like performance across signal-to-noise ratios (SNRs) under normal-hearing (NH) cochlear settings, achieving 50% digit recognition accuracy at −20.7 dB SNR. Results were comparable to eight NH participants on the same task who achieved 50% behavioral performance at −22 dB SNR. We also simulated medial olivocochlear reflex (MOCR) and auditory nerve fiber (ANF) loss, which worsened digit-recognition accuracy at lower SNRs compared to higher SNRs. Our simulated performance following ANF loss is consistent with the hypothesis that cochlear synaptopathy impacts communication in background noise more so than in quiet. Following the insult of various cochlear degradations, we implemented extreme and conservative adaptation through the DNN. At the lowest SNRs (<0 dB), both adapted models were unable to fully recover NH performance, even with hundreds of thousands of training samples. This implies a limit on performance recovery following peripheral damage in our human-inspired DNN architecture.

scholarly journals Real-time Hand-Gesture Recognition based on Deep Neural Network

2021 ◽  
Vol 102 ◽  
pp. 04009
Author(s):  
Naoto Ageishi ◽  
Fukuchi Tomohide ◽  
Abderazek Ben Abdallah
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Gesture Recognition ◽  
Deep Neural Network ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Robotic Control ◽  
Driver Assistance Systems ◽  
Fast Classification

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.

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