Anaerobic Threshold and EMG Activity During Incremental Cycling Exercise

Özgünen, Kerem; Arıca, Sami; Akkurt, Elif; Korkmaz, Selcen; Yazici, Zubeyde; Firat, Arsen; Kurdak, S. S.

doi:10.1249/00005768-200611001-00091

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although thresholding may not always provide the most accurate detection of EMG onset, it remains a popular choice in EMG signal processing. Nonetheless, there is lack of agreement among researchers on a standardized threshold method for EMG onset detection [ 46 ].…”

Section: Literature Review Resultsmentioning

confidence: 99%

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Carvalho,

Fernández,

del-Ama

et al. 2023

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data. Methods A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles. Results Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results. Conclusions This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.

show abstract

Section: Literature Review Resultsmentioning

confidence: 99%

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Carvalho,

Fernández,

del-Ama

et al. 2023

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…29 We therefore set the pre-defined threshold RMS EMG amplitude value as 1 mV. DCN-DBS was triggered only when the EMG amplitude was below the EMG threshold of 1 mV, which was at least 50% of the control baseline value (2.12 mV) 41 and was significantly higher than the average baseline RMS EMG value for ataxia mice (0.25 mV) (Figure 2B).…”

Section: Design and Optimization Of Fpga-based Closed-loop Dbs Contro...mentioning

confidence: 99%

Real‐time field‐programmable gate array‐based closed‐loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia

Kumar,

Zhou,

Wang

et al. 2024

CNS Neurosci Ther

View full text Add to dashboard Cite

AimsThe open‐loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real‐time field‐programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC‐specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits.MethodsWe implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open‐loop DCN‐DBS or closed‐loop DCN‐DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real‐time computation, was used for closed‐loop DCN‐DBS system.ResultsClosed‐loop DCN‐DBS was triggered only when symptomatic muscle EMG was detected in a real‐time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed‐loop DCN‐DBS was more effective than an open‐loop paradigm as it reduced the frequency of DBS.ConclusionOur real‐time FPGA‐based DCN‐DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.

show abstract

“…The selection strategy is mainly based on the determination of a speci c threshold value. The threshold choice lacks an agreement between the researchers [30].…”

Section: Feature Extractionmentioning

confidence: 99%

Timing and Classification of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

Ibraheem¹,

Adel²,

Balbaa³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Surface electromyogram (sEMG) processing and classi cation can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identi cation of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classi cation. To this end, sEMG signals were acquired from ve core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset, and time duration for the Transversus Abdominus (TrA), Vastus Medialis Obliquus (VMO), Gluteus Medius (GM), Vastus Lateralis (VL), and Multi dus Muscles (ML) were acquired to construct a classi cation model. The proposed classi cation model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space. The activation feature space of muscle timing is used to train several large margin classi ers to modulate muscle activations and account for such activation measurements. The fast large margin classi er achieved higher performance and faster convergence than support vector machines (SVMs) and other state-of-the-art classi ers. The proposed sEMG classi cation framework achieved an average accuracy of 98.8% after 7 s training time, improving other classi cation techniques in previous literature.

show abstract

Anaerobic Threshold and EMG Activity During Incremental Cycling Exercise

Cited by 3 publications

References 0 publications

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Real‐time field‐programmable gate array‐based closed‐loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia

Timing and Classification of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

Contact Info

Product

Resources

About