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 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%.

scholarly journals A PointNet-Based Solution for 3D Hand Gesture Recognition

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3226
Author(s):  
Radu Mirsu ◽  
Georgiana Simion ◽  
Catalin Daniel Caleanu ◽  
Ioana Monica Pop-Calimanu
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Deep Neural Network ◽  
Three Dimensional ◽  
Time Of Flight ◽  
Hand Gesture Recognition ◽  
Microsoft Kinect ◽  
Hand Gesture ◽  
Volume Of Interest ◽  
Hand Segmentation

Gesture recognition is an intensively researched area for several reasons. One of the most important reasons is because of this technology’s numerous application in various domains (e.g., robotics, games, medicine, automotive, etc.) Additionally, the introduction of three-dimensional (3D) image acquisition techniques (e.g., stereovision, projected-light, time-of-flight, etc.) overcomes the limitations of traditional two-dimensional (2D) approaches. Combined with the larger availability of 3D sensors (e.g., Microsoft Kinect, Intel RealSense, photonic mixer device (PMD), CamCube, etc.), recent interest in this domain has sparked. Moreover, in many computer vision tasks, the traditional statistic top approaches were outperformed by deep neural network-based solutions. In view of these considerations, we proposed a deep neural network solution by employing PointNet architecture for the problem of hand gesture recognition using depth data produced by a time of flight (ToF) sensor. We created a custom hand gesture dataset, then proposed a multistage hand segmentation by designing filtering, clustering, and finding the hand in the volume of interest and hand-forearm segmentation. For comparison purpose, two equivalent datasets were tested: a 3D point cloud dataset and a 2D image dataset, both obtained from the same stream. Besides the advantages of the 3D technology, the accuracy of the 3D method using PointNet is proven to outperform the 2D method in all circumstances, even the 2D method that employs a deep neural network.

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 Data Augmentation of Surface Electromyography for Hand Gesture Recognition

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4892
Author(s):  
Panagiotis Tsinganos ◽  
Bruno Cornelis ◽  
Jan Cornelis ◽  
Bart Jansen ◽  
Athanassios Skodras
Keyword(s):  
Gesture Recognition ◽  
Surface Electromyography ◽  
Classification Accuracy ◽  
Wavelet Decomposition ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Hand Gesture Recognition ◽  
Data Availability ◽  
Hand Gesture ◽  
Benchmark Datasets

The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.

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.

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