Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Ali, Asghar; Khan, Saad Jawaid; Azim, Fahad; Shakeel, Choudhary Sobhan; Hussain, Amatullah; Niazi, Imran Khan

doi:10.1177/09544119221074770

Cited by 24 publications

(16 citation statements)

References 104 publications

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“…Following filtering, the process pertaining to windowing was done in order to eliminate the spectral leakages. Previous studies including Asghar et al 32 and Waris et al 31 have used 200 ms window for eliminating spectral leakages. Hence, we empirically selected 200 ms as the window length as per the literature.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the integration of wavelet coefficients and machine learning techniques have shown robust results for upper limb motion classification. 32 Feature selection and evaluation criterion. We investigated the selection of features based on statistical index in this study to evaluate the distance between two scattered groups.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the integration of wavelet coefficients and machine learning techniques have shown robust results for upper limb motion classification. 32…”

Section: Methodsmentioning

confidence: 99%

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Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method

Ali

Khan

Azim

et al. 2022

Proc Inst Mech Eng H

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Prostheses control using electromyography signals have shown promising aspects in various fields including rehabilitation sciences and assistive technology controlled devices. Pattern recognition and machine learning methods have been observed to play a significant role in evaluating features and classifying different limb motions for enhanced prosthetic executions. This paper proposes feature extraction and evaluation method using intramuscular electromyography (iEMG) signals at different arm positions and hand postures based on the RES Index value statistical criterion method. Sixteen-time domain features were selected for the study at two main circumstances; fixed arm position (FAP) and fixed hand posture (FHP). Eight healthy male participants (30.62 ± 3.87 years) were asked to execute five motion classes including hand grip, hand open, rest, hand extension, and hand flexion at four different arm positions that comprise of 0°, 45°, 90°, and 135°. The classification process is accomplished via the application of the k-nearest neighbor (KNN) classifier. Then RES index was calculated to investigate the optimal features based on the proposed statistical criterion method. From the RES Index, we concluded that Variance (VAR) is the best feature while WAMP, Zero Crossing (ZC), and Slope Sign Change (SSC) are the worst ones in FAP conditions. On the contrary, we concluded that Average Amplitude Change (AAC) is the best feature while WAMP and Simple Square Integral (SSI) resulted in least RES Index values for FHP conditions. The proposed study has possible iEMG based applications such as assistive control devices, robotics. Also, working with the frequency domain features encapsulates the future scope of the study.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method

Ali

Khan

Azim

et al. 2022

Proc Inst Mech Eng H

Self Cite

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“…Several reviews about intention recognition have been published, and they focus on EMG signals [19], [20], [21], and neural networks [22]. However, these reviews have not conducted detailed studies with statistical data of articles.…”

Section: Introductionmentioning

confidence: 99%

Visualized Analysis of Intention Recognition Research Based on CiteSpace

2022

AJETS

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Intention recognition has improved the accuracy and speed of interactions between human and machine. However, due to the rapid development, the diversity of research content and methods, and the wide range of applications, researches in this field lack systematic integration. Based on 1234 selected papers, this paper applied visualization methods in CiteSpace to figure out significant country, institution, journal, reference and keywords to demonstrate the developments and research hotspots from 2011 to 2021. He Huang and Aaron J. Young are thought to be the most productive authors. According to the keywords analysis, "Hidden Markov Model", "networks", "feature extraction" are current hotspots. Furthermore, the deficiencies and future directions have been indicated by conducting this composite analysis.

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“…The EMG signal carries a significant quantity of data about limb movements and functionality despite its complexity. As a result, it can be successfully used in prosthetic [3,4], rehabilitation or clinical posture analysis and diagnosis of neuromuscular illnesses [5][6][7].…”

mentioning

confidence: 99%

Recognition of Hand Gestures using Wavelet Packet Transform and Cascaded Feed Forward Neural Networks

Jayasree

2023

Preprint

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This paper presents a noval framework that classifies finger movements automatically using Wavelet Transform and its derivatives by capturing statistical features from the discrete time Electromyogram (EMG) signals. In the suggested method, wavelet-based denoising is used to separate out the subject's EMG signals, and then Discrete Wavelet Transform (DWT) and Wavelet Packet Transform are used to decompose the signals and extract their key characteristics (WPT). The derivatives of the feature sets are employed to analyse the correlation among them. This method is motivated by the surveillance that there exists a distinctive correlation between the different features of the samples of the signals extracted at various frequency levels. Experimentally, it was perceived that this correlation varies from signal to signal. Both Feed forward and Cascaded Feed forward Artificial Neural Networks (ANN) are used for classification. Experiments show that the proposed method significantly improves the classification rate. The performance of the suggested wavelet-based features and their derivatives in combination with ANN and trained with the Levenberg-Marquardt algorithm was evaluated by comparing the simulation results for various sets of features. Comparing the new method benefits to earlier traditional methods in terms of classification performance helped to further highlight their advantages. These experimental findings demonstrate that the suggested approach performs admirably in classifying finger movements based on EMG signal patterns. The suggested methodology also helps clinicians increase the reliability of myoelectric pattern recognition.

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Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Cited by 24 publications

References 104 publications

Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method

Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method

Visualized Analysis of Intention Recognition Research Based on CiteSpace

Recognition of Hand Gestures using Wavelet Packet Transform and Cascaded Feed Forward Neural Networks

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