Intelligent Computation in Grasping Control of Dexterous Robot Hand

Ding, Weiliang; Li, Gongfa; Jiang, Guozhang; Fang, Yinfeng; Ju, Zhaojie; Liu, Honghai

doi:10.1166/jctn.2015.4642

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…The second part is a random process, and the matrix B represents the probability distribution description of the state. Hidden Markov models can be represented by quintuple [50]- [52]: A-The time-independent state transition probability matrix is:A = a ij , a ij = P q t+1 = s j q t = s i , 1 ≤ i, j ≤ N , and N j=1 a ij = 1; B-state output probability matrix is:…”

Section: Model Trainingmentioning

confidence: 99%

Dynamic Gesture Recognition in the Internet of Things

Jiang

et al. 2019

IEEE Access

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Gesture recognition based on computer vision has gradually become a hot research direction in the field of human-computer interaction. The field of human-computer interaction is an important direction in the Internet of Things (IoTs) technology. Human-computer interaction through gestures is the direction of continuous research on IoTs technology. In recent years, the Kinect sensor-based gesture recognition method has been widely used in gesture recognition, because it can separate gestures from complex backgrounds and is less affected by illumination and can accurately track and locate gesture motions. At present, the Kinect sensor needs to be further improved on the recognition of complex gesture movements, especially the problem that the recognition rate of dynamic gestures is not high, which hinders the development of humancomputer interaction under the IoTs technology. In this paper, based on the above problems, the Kinectbased gesture recognition is analyzed in detail, and a dynamic gesture recognition method based on HMM and D-S evidence theory is proposed. Based on the original HMM, the tangent angle and gesture change at different moments of the palm trajectory are used as the characteristics of the complex motion gesture, and the dimension of the trajectory tangent is reduced by the number of quantization codes. Then, the parameter model training of HMM is completed. Finally, combined with D-S evidence theory, combinatorial logic is judged, dynamic gesture recognition is carried out, and a better recognition effect is obtained, which lays a good foundation for human-computer interaction under the IoTs technology. INDEX TERMSGesture recognition, hidden Markov model (HMM), D-S evidence theory, Internet of Things (IoT).

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Section: Model Trainingmentioning

confidence: 99%

Dynamic Gesture Recognition in the Internet of Things

Jiang

et al. 2019

IEEE Access

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“…The sEMG is random in nature, which is made up of the action potential of the motion unit [1], [9]. These action potentials exist in the electrode pick-up area and the detected voltage is the sum of the action potentials of the single electrode elements [10].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition

Jiang

et al. 2019

IEEE Access

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160

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Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

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“…However, due to the fewer features, the use of this feature requires a larger training set, which makes its practical application difficult. In literature [6], In order to improve the fineness of the description of the features of Hu invariant moments, three characterization vector formulas have been added for recognition research. In literature [7] fuses the information of the first four Hu moments and the contour of the gesture, which improves the accuracy of recognition.…”

Section: Related Workmentioning

confidence: 99%

“…The speed of the depth image flow is 30 frame / s, which can restore the surrounding environment in real time, online and 3D. Compared with some laser sensors, Kinect sensors cannot only get RGB and depth images at the same time, but also have strong anti-interference and low cost to the visible spectrum, so it is very suitable for some indoor navigation with low accuracy [6].…”

Section: Fig 1 Kinect Sensormentioning

confidence: 99%

Jointly network: a network based on CNN and RBM for gesture recognition

Cheng

Sun

et al. 2018

Neural Comput & Applic

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Hand belongs to non-rigid objects and is rich in variety, making gesture recognition more difficult. The essence of dynamic gesture recognition is the classification and recognition of single-frame still images. Therefore, this paper mainly focuses on static gesture recognition. At present, there are some problems in gesture recognition, such as accuracy, real-time or poor robustness. To solve the above problems, in this paper, the Kinect sensor is used to obtain the color and depth gesture samples, and the gesture samples are processed. On this basis, a jointly network of CNN and RBM is proposed for gesture recognition. It mainly uses superposed network of multiple RBMs to carry out unsupervised feature extraction and combined with supervised feature extraction of CNN. Finally, these two features are combined to classify them. The simulation results show that the proposed jointly network has a better performance in identifying simple background gesture samples, and the recognition capability of gesture samples in complex background needs to be improved.

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Intelligent Computation in Grasping Control of Dexterous Robot Hand

Cited by 11 publications

References 17 publications

Dynamic Gesture Recognition in the Internet of Things

Dynamic Gesture Recognition in the Internet of Things

Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition

Jointly network: a network based on CNN and RBM for gesture recognition

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