Myoelectric signal analysis using neural networks

Kelly, M.F.; Parker, P.A.; Scott, Robert N.

doi:10.1109/51.62909

Cited by 15 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A wide range of feature sets, including time-domain [1-7] and frequency domain [8], combined with a wide range of classifiers, including fuzzy logic [5, 9-11], neural networks [2, 3, 12-16], and Bayesian statistics [16-18], have yielded low classification error (error) on able-bodied subjects. Several studies have produced low error on subjects with an amputation [9, 13, 14, 16, 17, 19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms

Sensinger

Lock

Kuiken

2009

IEEE Trans. Neural Syst. Rehabil. Eng.

203

171

View full text Add to dashboard Cite

Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms

Sensinger

Lock

Kuiken

2009

IEEE Trans. Neural Syst. Rehabil. Eng.

203

171

View full text Add to dashboard Cite

show abstract

“…Discrete limb-movement types or “states” (e.g., elbow flexion vs extension) are often identified using pattern-recognition approaches such as linear discriminant analysis [13], fuzzy logic [14–15], and artificial neural networks (ANNs) [4,16]. Alternatively, ANNs have been used to predict continuous movement trajectories using EMG signals rather than discrete states.…”

Section: Introductionmentioning

confidence: 99%

Electromyogram-based neural network control of transhumeral prostheses

Pulliam

Lambrecht

Kirsch³

2011

JRRD

View full text Add to dashboard Cite

Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG) recording system with a wireless telemetry system that communicates with the patient’s prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination) based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.

show abstract

“…Logo, é amplamente utilizado na área da saúde em diagnósticos de doenças neuromusculares (Wood et al, 2001;Drost et al, 2001), análise de marcha, acompanhamento fisioterapêutico e análise de fadiga muscular (Zwarts & Stegeman, 2003), ativação de próteses (Graupe & Cline, 1975), entre outros. (Kelly et al, 1990;Hiraiwa et al, 1990;Gazzoni et al, 2004). Em 2004, Farina e colaboradores (Farina et al, 2004b) conseguiram separar sinais EMG de dois grupos musculares distintos utilizando técnicas de BSS que utilizam estatística de segunda ordem.…”

Section: E Capítulounclassified

Decomposição de sinais eletromiográficos de superfície misturados linearmente utilizando análise de componentes independentes

Almeida¹

View full text Add to dashboard Cite

Electromyography is a clinical practice that provides information regarding the physiological condition of the neuromuscular system, which includes the analysis of the contractile functional unit of the neuromuscular system, known as motor unit. The electromyographic signal is an electrical signal resultant from ionic transient regarding motor unit action potentials captured by invasive or non-invasive electrodes. Invasive electrodes are able to detect action potentials of even one motor unit, although the procedure is time consuming and uncomfortable. Surface electrodes enable detecting action potential noninvasively, although the detected signal is a mixture of action potentials from several motor units within the detection area of the electrode, resulting in a complex interference pattern which is difficult to interpret. Blind Source Separation techniques, such as Independent Component Analysis, have proven effective for decomposing surface electromyographic signals into the constituent motor unit action potentials. The objective of this project was to develop a system in order to capture surface myoelectric signals and to analyze the viability for decomposing intramuscular myoelectric signals that were mixed linearly, using independent component analyzes. The system includes an electrode matrix with up to seven channels, a preprocessing module, a software for controlling surface myoelectric signals capture, and the FastICA algorithm in MATLAB® for the intramuscular myoelectric signals decomposition. The results show that the system was able to capture surface myoelectric signals and was capable of decomposing the intramuscular myoelectric signals that were previously linearly mixed.

show abstract

Myoelectric signal analysis using neural networks

Cited by 15 publications

References 12 publications

Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms

Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms

Electromyogram-based neural network control of transhumeral prostheses

Decomposição de sinais eletromiográficos de superfície misturados linearmente utilizando análise de componentes independentes

Contact Info

Product

Resources

About