Supervised Hierarchical Bayesian Model-Based Electomyographic Control and Analysis

Han, Hongbin; Jo, Sungho

doi:10.1109/jbhi.2013.2284476

Cited by 19 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the advances in recent sEMG signal classification for the activation of auxiliary devices [3,4,5], optimal signal processing strategies and portable devices development yet face several restrictions. Despite the deterministic range of the sEMG signal in frequency and amplitude, factors such as subject dependency and lack of signal repeatability often preclude efficient and reliable myoelectric pattern recognition and control since the first studies in the area [6,7], making the optimal sEMG signal classification an arduous task from a machine learning perspective [8,9,10,11,12]. Thus, the natural control of assistive devices based on sEMG activation is a field of constant expansion in biomedical engineering.…”

Section: Introductionmentioning

confidence: 99%

Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Cene

Machado

Balbinot

2019

Sensors

View full text Add to dashboard Cite

Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reliable forms of our Extreme Learning Machines (ELM) classifiers, used to maintain a more consistent signal classification. To perform the signal processing, we explore the use of a stochastic filter based on the Antonyan Vardan Transform (AVT) in combination with two variations of our Reliable classifiers (denoted R-ELM and R-Regularized ELM (RELM), respectively), to derive a reliability metric from the system, which autonomously selects the most reliable samples for the signal classification. To validate and compare our database and classifiers with related papers, we performed the classification of the whole of Databases 1, 2, and 6 (DB1, DB2, and DB6) of the NINAProdatabase. Our database presented consistent results, while the reliable forms of ELM classifiers matched or outperformed related papers, reaching average accuracies higher than 99 % for the IEEdatabase, while average accuracies of 75 . 1 % , 79 . 77 % , and 69 . 83 % were achieved for NINAPro DB1, DB2, and DB6, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Cene

Machado

Balbinot

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…In a previous study, we applied nuclear magnetic resonance (NMR) tracer technology to quantitatively measure the normal rat caudate nucleus, thalamus, occipital cortex proliferation, substantia nigra, and clearance parameters (9,10). The current study firstly applied NMR tracer technology to quantitatively compare diffusion and clearance parameters in 10- and 20-day C6 glioma models, analyzing factors that affected diffusion and clearance of extracellular matrix (ECM) components by immunohistochemistry and western blot.…”

Section: Introductionmentioning

confidence: 99%

Extracellular diffusion quantified by magnetic resonance imaging during rat C6 glioma cell progression

Song

Luo

Dong

et al. 2017

Braz J Med Biol Res

View full text Add to dashboard Cite

Solution reflux and edema hamper the convection-enhanced delivery of the standard treatment for glioma. Therefore, a real-time magnetic resonance imaging (MRI) method was developed to monitor the dosing process, but a quantitative analysis of local diffusion and clearance parameters has not been assessed. The objective of this study was to compare diffusion into the extracellular space (ECS) at different stages of rat C6 gliomas, and analyze the effects of the extracellular matrix (ECM) on the diffusion process. At 10 and 20 days, after successful glioma modeling, gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) was introduced into the ECS of rat C6 gliomas. Diffusion parameters and half-life of the reagent were then detected using MRI, and quantified according to the mathematical model of diffusion. The main ECM components [chondroitin sulfate proteoglycans (CSPGs), collagen IV, and tenascin C] were detected by immunohistochemical and immunoblot analyses. In 20-day gliomas, Gd-DTPA diffused more slowly and derived higher tortuosity, with lower clearance rate and longer half-life compared to 10-day gliomas. The increased glioma ECM was associated with different diffusion and clearance parameters in 20-day rat gliomas compared to 10-day gliomas. ECS parameters were altered with C6 glioma progression from increased ECM content. Our study might help better understand the glioma microenvironment and provide benefits for interstitial drug delivery to treat brain gliomas.

show abstract

“…As example, Ahsan et al [5] presented the detection of four hand motions using an ANN. Additionally, considering the advantages of Fuzzy Logic combined with the power of adaptation of an ANN, a Neuro-Fuzzy algorithm for myoelectric control has been proposed [1,17] for the intelligent control of a prosthesis. Also, a hierarchical Neuro-Fuzzy [7] controller has been found to be adapting well on people who generate different muscle activity levels.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Method for sEMG Movement Classification Based on Continuous Optimal Electrode Assortment

Favieiro

Cene

Balbinot

2016

BJIC

View full text Add to dashboard Cite

Abstract-Surface electromyography (sEMG) analysis is becoming increasingly popular in a broad variety of applications. Despite satisfactory classification rates are frequently obtained through supervised machine learning (ML) algorithms, there are some issues mostly related to the data acquisition which are not properly addressed in current studies. In this paper we present a method capable of mitigate the noise in the sEMG acquisition caused mainly by loose or misplaced non-invasive electrodes. To address this issue we propose a stage of pre-processing capable of being adapted on a variety of classifiers. The proposed method is capable of identify this two anomalies in the signal and provide the data to retrain the classifier, discarding the problematic channels. Once the method is retrained using only the most relevant channels it is possible to increase the accuracy rate of the ML method. The method was tested on a database containing five ablebodied subjects and four amputee subjects of both sexes. The average classification accuracy for the adaptive input selection method was 83,96  6,5% for the able-bodied subjects and 61,15  7,7% for the amputees subjects against 72,06  8,0% in ablebodied subjects and 39,77  10,6% for the amputees subjects considering the non-adaptive approach. Both systems make use of the proposed method to classify 9 distinguish upper-limb movements with different degrees of freedom.Index Terms-electrode assortment, upper-limb signal, neural network, auto-adaptive methods, surface electromyography

show abstract

Supervised Hierarchical Bayesian Model-Based Electomyographic Control and Analysis

Cited by 19 publications

References 33 publications

Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Extracellular diffusion quantified by magnetic resonance imaging during rat C6 glioma cell progression

Self-adaptive Method for sEMG Movement Classification Based on Continuous Optimal Electrode Assortment

Contact Info

Product

Resources

About