Adaptive detection of Ahead-sEMG based on short-time energy of local-detail difference and recognition in advance of upper-limb movements

Li, Xiuping; Liang, Shili; Yan, Shifeng; Ryu, JongSong; Wu, Yanxia

doi:10.1016/j.bspc.2023.104752

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Anomaly detection, identification of unusual muscle activation pattern are some applications of DBSCAN. This method has been used to eliminate the data that deviates from the center cluster and finally apply the ML techniques for movements detection [35]. DBSCAN clustering has been compared with other clustering method observing different patterns in a movement detection study [36].…”

Section: Density-based Spatial Clustering Of Applications With Noise ...mentioning

confidence: 99%

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An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Ojha

2023

Extsv. Rev.

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Electromyography (EMG) is about studying electrical signals from muscles and can provide a wealth of information on the function, contraction, and activity of your muscles. In the field of EMG pattern recognition, these signals are used to identify and categorize patterns linked to muscle activity. Various machine learning (ML) methods are used for this purpose. Successful detection of these patterns depends on using effective signal-processing techniques. It is crucial to reduce noise in EMG for accurate and meaningful information about muscle activity, improving signal quality for precise assessments. ML tools such as SVMs, neural networks, KNNs, and decision trees play a crucial role in sorting out complex EMG signals for different pattern recognition tasks. Clustering algorithms also help analyze and interpret muscle activity. EMG and ML find diverse uses in rehabilitation, prosthetics, and human-computer interfaces, though real-time applications come with challenges. They bring significant changes to prosthetic control, human-computer interfaces, and rehabilitation, playing a vital role in pattern recognition. They make prosthetic control more intuitive by understanding user intent from muscle signals, enhance human-computer interaction with responsive interfaces, and support personalized rehabilitation for those with motor impairments. The combination of EMG and ML opens doors for further research into understanding muscle behavior, improving feature extraction, and advancing classification algorithms.

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Section: Density-based Spatial Clustering Of Applications With Noise ...mentioning

confidence: 99%

“…; Vol. 3, Issue 1, pp: [24][25][26][27][28][29][30][31][32][33][34][35][36][37]2023 can handle this type of data. ML models can recognize EMG signals that are associated with certain conditions, helping in early detection and treatment [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Ojha

2023

Extsv. Rev.

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An end-to-end hand action recognition framework based on cross-time mechanomyography signals

Zhang,

Li,

Zhang

et al. 2024

Complex Intell. Syst.

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The susceptibility of mechanomyography (MMG) signals acquisition to sensor donning and doffing, and the apparent time-varying characteristics of biomedical signals collected over different periods, inevitably lead to a reduction in model recognition accuracy. To investigate the adverse effects on the recognition results of hand actions, a 12-day cross-time MMG data collection experiment with eight subjects was conducted by an armband, then a novel MMG-based hand action recognition framework with densely connected convolutional networks (DenseNet) was proposed. In this study, data from 10 days were selected as a training subset, and the remaining data from another 2 days were used as a test set to evaluate the model’s performance. As the number of days in the training set increases, the recognition accuracy increases and becomes more stable, peaking when the training set includes 10 days and achieving an average recognition rate of 99.57% (± 0.37%). In addition, part of the training subset is extracted and recombined into a new dataset and the better classification performances of models can be achieved from the test set. The method proposed effectively mitigates the adverse effects of sensor donning and doffing on recognition results.

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The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms

Zhu,

Zhao

2024

Biomedical Engineering / Biomedizinische Technik

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Objectives To overcome the limitations of traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients, this study aims to propose a surface electromyographic (sEMG) based method for reliable and accurate quantitative assessment of lip closure ability. Methods A total of 21 volunteers (16 patients and 5 healthy subjects, aged 8–16) were included in the study. Three nonlinear onset detection algorithms – Teager–Kaiser Energy (TKE) operator, Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn) – were compared for their ability to identify lip closure in sEMG signals. Lip Closure EMG Activity Index (LCEAI) was proposed based on the action segments detected by the best performing algorithm for the quantitative assessment of lip closure. Results The results indicated that FuzzyEn had the highest lip closure identification rate at 93.78 %, the lowest average onset delay of 47.50 ms, the lowest average endpoint delay of 73.10 ms, and the minimal time error of 111.61 ms, exhibiting superior performance. The calculation results of the LCEAI closely corresponded with the actual degree of lip closure in patients. Conclusions The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.

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Adaptive detection of Ahead-sEMG based on short-time energy of local-detail difference and recognition in advance of upper-limb movements

Cited by 5 publications

References 35 publications

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

An end-to-end hand action recognition framework based on cross-time mechanomyography signals

The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms

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