Neural muscle activation detection: A deep learning approach using surface electromyography

Khowailed, Iman Akef; Abotabl, Ahmed Attia

doi:10.1016/j.jbiomech.2019.109322

Cited by 20 publications

(15 citation statements)

References 15 publications

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“…In the last decades, the extraction of the onset/offset timing of the muscular activity from sEMG signals has found a great interest in different research areas, including neurorobotics and myoelectric control of prostheses [ 3 ], motor rehabilitation and sport science [ 2 ], and human–machine interaction [ 4 ]. Accordingly, several approaches have been proposed in the literature to extract the onset and offset time-instants of muscle activations during human movements [ 12 , 15 – 26 ]. The majority of the published detectors are threshold-based approaches, such as the single-threshold detector based on the Teager–Kaiser Energy Operator [ 29 , 30 ] and the double-threshold statistical detector proposed by Bonato et al .…”

Section: Discussionmentioning

confidence: 99%

“…In an attempt to increase the accuracy of the temporal analysis of muscle activations, several methods have been proposed in the literature, from the simplest approaches based on single-threshold detectors [ 12 ] to more complex approaches based on wavelet transform [ 13 , 14 ], statistical optimal decision criteria [ 15 , 16 ], or deep learning techniques [ 17 – 26 ]. One of the most widely used ways to detect the timing of muscle activations from sEMG signals is using a double-threshold detector, such as the double-threshold statistical detector by Bonato et al .…”

Section: Introductionmentioning

confidence: 99%

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Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Ghislieri

Cerone

Knaflitz

et al. 2021

J NeuroEngineering Rehabil

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Background The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks. Methods First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis. Results The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR. Conclusions The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Ghislieri

Cerone

Knaflitz

et al. 2021

J NeuroEngineering Rehabil

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“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

172

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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“…Fig. 1 EMG signal processing steps (LMS) algorithm is the best cancelation technique [4]. The muscle activation timing of an sEMG signal is detected by obtaining the Motor Unit Action Potential (MUAP).…”

Section: Classificaɵonmentioning

confidence: 99%

Recent Trends in Electromyography Signal Processing of Neuromuscular Diseases: An Outlook

Emimal

Hans²,

Inbamalar³

et al. 2021

Advanced Computing and Intelligent Technologies

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This paper presents a bibliometric review of several techniques applied to the EMG signals. We reviewed research papers, which were specifically applied for the EMG signals. The EMG signal contains a huge amount of data, thus the EMG signal research grabs the significance of advanced techniques and analysis of data, which are capable of handling 'Big Data'. Several noise reduction techniques were discussed and it was found that the wavelet-based noise reduction is a promising technique for EMG classification. More prominent feature extraction and classification techniques and their performance were also reviewed. The modern EMG signal analysis mainly emphasizes feature learning, which is specifically 'deep learning', which combines feature extraction and classification, also to improve classification accuracy. A performance analysis of Convolutional Neural Network (CNN) was done in the later sections.

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Neural muscle activation detection: A deep learning approach using surface electromyography

Cited by 20 publications

References 15 publications

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Deep Learning in Physiological Signal Data: A Survey

Recent Trends in Electromyography Signal Processing of Neuromuscular Diseases: An Outlook

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