Gesture Recognition Based on Kinect and sEMG Signal Fusion

Sun, Ying; Li, Cuiqiao; Li, Gongfa; Jiang, Guozhang; Du, Jiang; Liu, Honghai; Zheng, Zhigao; Shu, Wanneng

doi:10.1007/s11036-018-1008-0

Cited by 93 publications

(57 citation statements)

References 33 publications

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“…Wavelet threshold noise reduction is based on wavelet decomposition, and wavelet decomposition is a multi-frequency hierarchical decomposition of sEMG through a series of wavelet basis functions or specific wavelet basis functions, and then sets a certain threshold to deal with specific frequency components, and finally integrated into the original signal to achieve noise reduction [34][35][36][37].…”

Section: Wavelet Threshold Noise Reduction Of 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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107

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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: Wavelet Threshold Noise Reduction Of 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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107

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“…With the development of multi sensor technology, electronic information technology, pattern recognition technology and biomedical engineering, study on surface electromyography (SEMG) controlled prosthetic hand has gained growing attention (Sun et al, 2018). The prosthetic hand based on SEMG signals has become a hot topic in the field of prosthetic control.…”

Section: Semg Signalsmentioning

confidence: 99%

Tactile sensing and feedback in SEMG hand

Kong

Tao

et al. 2018

IJCSM

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Active hand prostheses controlled using surface electromyography (SEMG) signals have been used for decades to restore the grasping function. Amputees with myoelectric hands wish to control the prostheses according to their own will and act like human hands as much as possible. Therefore, substantial research efforts have been put forth to advance the control of myoelectric hands. However, the tactile sensing and feedback of the myoelectric hands are still missing, thus limit hand grasp capabilities. In fact, integration of tactile sensing and feedback with hand prostheses plays an important role in improving the manipulation performance and enhancing perceptual embodiment for users. This paper reviews current state-of-the-art of tactile sensing technologies, including tactile sensor types and integration methods. Then, introduces the basic theory of SEMG signals and presents an overview of the sensory feedback employed to prosthetic hand. The paper concludes with a detailed discussion of challenging issues and future developments.

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“…The Internet of Things technology mainly refers to the development of corresponding functions in the real world to transmit, process and execute smart data. This requires the Internet of Things to have the corresponding computing power and perceptual power, so that these real-world data can be converted into smart data, and ultimately achieve mutual interaction between people and things [1][2]. The research background of this paper is the somatosensory interaction technology based on the overall environment of the Internet of Things.…”

Section: Introductionmentioning

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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Gesture Recognition Based on Kinect and sEMG Signal Fusion

Cited by 93 publications

References 33 publications

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Tactile sensing and feedback in SEMG hand

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

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