Classification of hand movements using variational mode decomposition and composite permutation entropy index with surface electromyogram signals

Xiao, Feiyun; Yang, Depeng; Lv, Zhongming; Guo, Xiaohui; Liu, Zhengshi; Wang, Yong

doi:10.1016/j.future.2019.11.025

Cited by 30 publications

(21 citation statements)

References 42 publications

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“…e proposed model detects hands in real time by decreasing calculation utilizing a single sensor instead of fusing manifold sensors, enables precise tracking, and enhances hand tracking precision. Xiao et al [26] proposed the variational mode decomposition and composite permutation entropy index (VMD-CPEI) method to classify hand movements.…”

Section: Related Work and Features Of This Research Articlementioning

confidence: 99%

Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Janarthanan¹,

Venkatesh²,

Joshi

et al. 2021

Scientific Programming

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Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

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Section: Related Work and Features Of This Research Articlementioning

confidence: 99%

Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Janarthanan¹,

Venkatesh²,

Joshi

et al. 2021

Scientific Programming

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show abstract

“…Hand gesture prediction can predict the instant human activity intention [ 11 ] and is a convenient means for the communication between human and machines [ 12 ]. The results of hand recognition can be applied to artificial limb control, wearable exoskeleton and human–robot interactions [ 13 ]. However, vision-based methods usually have occlusion and delay problems caused by the complexity of hand motions and the limitations of vision-based methods.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Hand Grasping Prediction and Control via Postural Synergy Basis Extraction

Liu

Yin

et al. 2022

Sensors

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The prediction of hand grasping and control of a robotic manipulator for hand activity training is of great significance to assist stroke patients to recover their biomechanical functions. However, the human hand and the figure joints have multiple degrees of freedom; therefore, it is complex to process and analyze all the collected data in hand modeling. To simplify the description of grasping activities, it is necessary to extract and decompose the principal components of hand actions. In this paper, the relationships among hand grasping actions are explored by extracting the postural synergy basis of hand motions, aiming to simplify hand grasping actions and reduce the data dimensions for robot control. A convolutional neural network (CNN)-based hand activity prediction method is proposed, which utilizes motion data to estimate hand grasping actions. The prediction results were then used to control a stimulated robotic model according to the extracted postural synergy basis. The prediction accuracy of the proposed method for the selected hand motions could reach up to 94% and the robotic model could be operated naturally based on patient’s movement intention, so as to complete grasping tasks and achieve active rehabilitation.

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“…The probability distribution of ordinal patterns (OP) implicitly captures information about the temporal structure of the time series and thus allows the extraction of several promising ordinal pattern-based indicators. These indicators are useful in a growing number of applications including biomedical signal and image processing [ 6 , 10 , 11 , 15 , 31 , 33 , 38 ], fault bearing diagnosis [ 30 , 39 , 40 , 41 , 42 , 43 , 44 ], financial time series analysis [ 7 ] and engineering physics [ 18 , 35 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

“…The PE is theoretically related to the well-known measure of dynamical systems complexity, the Kolmogorov–Sinai entropy [ 9 ]. A large number of time series processing tasks were performed using the PE tool [ 16 , 26 , 38 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy

Jabloun

Ravier

Buttelli

2022

Entropy

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Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessing and especially to data-driven signal decomposition-based MPE. The empirical mode decomposition, variational mode decomposition, singular spectrum analysis-based decomposition and empirical wavelet transform are considered. We identify possible pitfalls in the interpretation of PE values induced by these nonlinear preprocessing, and hence, we contribute to improving the PE interpretation. The simulated dataset of representative processes such as white Gaussian noise, fractional Gaussian processes, ARMA models and synthetic sEMG signals as well as real-life sEMG signals are tested.

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Classification of hand movements using variational mode decomposition and composite permutation entropy index with surface electromyogram signals

Cited by 30 publications

References 42 publications

Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Computer Vision for Human-Computer Interaction Using Noninvasive Technology

CNN-Based Hand Grasping Prediction and Control via Postural Synergy Basis Extraction

On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy

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