Hand gesture recognition based on convolution neural network

Li, Gongfa; Tang, Tianhao; Sun, Ying; Kong, Jianyi; Jiang, Guozhang; Du, Jiang; Tao, Bo; Xu, Shuang; Liu, Honghai

doi:10.1007/s10586-017-1435-x

Cited by 175 publications

(83 citation statements)

References 33 publications

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“…In this paper, the noise is classified and the noise is processed with digital filtering and wavelet transform, and the true signal wave is reduced as much as possible. At the same time, feature selection and feature vector are selected [26][27][28]. The formation is also the key point for the implementation of the rehabilitation equipment and intelligent prosthetic control strategy.…”

Section: Noise Reduction Of Original Surface Emg Signalmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

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In the current development and design of sports rehabilitation equipment or biomimetic prostheses, in addition to pay attention to the development and design of the structure, the more core is how to realize the accurate and effective control of the rehabilitation equipment or intelligent prosthesis, and the current research is based on data process and pattern recognition. This paper designs 9 kinds of actions that can react effectively to the function of the hand and extracts the original EMG signals, which are based on the sEMG of the forearm muscles of human hand movement, and uses the 20 order comb filter and wavelet threshold to preprocess the signal, and uses the root mean square, wavelength and nonlinear characteristics sample entropy in time domain as three eigenvalues to construct the input feature vectors of the subsequent action classifier. Finally, the recognition of the hand movements is realized successfully through GRNN and SVM. The recognition rate is 98.64% in SVM classifier and 96.27% in GRNN classifier. Experimental results show that the SVM classifier is better than the GRNN classifier.

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Section: Noise Reduction Of Original Surface Emg Signalmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

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“…In recent years, the use of data gloves for gesture recognition has triggered a wave of gesture recognition research [4][5]. Some scholars propose a Kinect-based gesture recognition method that uses FEMD algorithm to achieve stable static gesture recognition, but the algorithm requires a large of data for training [6][7]. Some researchers have combined BPNN and PSO algorithms on the basis of traditional algorithms, which greatly reduces the training time and improves the accuracy of gesture recognition [8].…”

Section: Related Workmentioning

confidence: 99%

Research on gesture recognition of smart data fusion features in the IoT

Tan

Sun

et al. 2019

Neural Comput & Applic

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With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.

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“…Li et al [18] used CNN for the detection of gestures along with characteristics of CNN to avoid the overall feature extraction process, which reduces the trained parameters quantity and helps to develop a system of unsupervised learning. The results from the study indicated an overall accuracy of 98.52% as they developed a semi-supervised model through support vector machine (SVM).…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks

2020

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The convolutional neural network (CNN) algorithm is one of the efficient techniques to recognize hand gestures. In human–computer interaction, a human gesture is a non-verbal communication mode, as users communicate with a computer via input devices. In this article, 3D micro hand gesture recognition disparity experiments are proposed using CNN. This study includes twelve 3D micro hand motions recorded for three different subjects. The system is validated by an experiment that is implemented on twenty different subjects of different ages. The results are analysed and evaluated based on execution time, training, testing, sensitivity, specificity, positive and negative predictive value, and likelihood. The CNN training results show an accuracy as high as 100%, which present superior performance in all factors. On the other hand, the validation results average about 99% accuracy. The CNN algorithm has proven to be the most accurate classification tool for micro gesture recognition.

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Hand gesture recognition based on convolution neural network

Cited by 175 publications

References 33 publications

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Research on gesture recognition of smart data fusion features in the IoT

Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks

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