Bayesian Filtering of Myoelectric Signals

2007 ◽  
Vol 97 (2) ◽  
pp. 1839-1845 ◽  
Author(s):  
Terence D. Sanger
Keyword(s):  
Signal To Noise Ratio ◽  
Jump Process ◽  
Poor Quality ◽  
Joint Torque ◽  
Conditional Density ◽  
Time Variability ◽  
Bayesian Filtering ◽  
Emg Signal ◽  
On Line ◽  
Response To Change

Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

scholarly journals Methods of Power Line Interference Elimination in EMG Signal

2019 ◽  
Vol 40 ◽  
pp. 64-70
Author(s):  
Martina Ladrova ◽  
Radek Martinek ◽  
Jan Nedoma ◽  
Marcel Fajkus
Keyword(s):  
Signal To Noise Ratio ◽  
Notch Filter ◽  
Correlation Coefficients ◽  
Power Line ◽  
Shape Distortion ◽  
Emg Signal ◽  
Wide Range ◽  
Interference Elimination ◽  
Power Line Interference ◽  
Line Interference

Electromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.

scholarly journals Real-Time Estimation of Some Thermodynamics Properties During a Microwave Heating Process

2017 ◽  
Vol 21 (2) ◽  
Author(s):  
Edgar Garcia ◽  
Ivan Amaya ◽  
Rodrigo Correa
Keyword(s):  
Real Time ◽  
Physicochemical Parameters ◽  
Signal To Noise Ratio ◽  
Estimation Procedure ◽  
Original Data ◽  
Marquardt Method ◽  
On Line ◽  
Electronic Instrumentation ◽  
Uniform Electromagnetic Field ◽  
Real Temperature

<p class="MsoNormal"><span lang="EN-US">This work considers the prediction in real time of physicochemical parameters of a sample heated in a uniform electromagnetic field. The thermal conductivity (K)</span><!--[if gte msEquation 12]><m:oMath><i style='mso-bidi-font-style:normal'><span lang=EN-US style='font-family:"Cambria Math","serif"'><m:r>(</m:r><m:r>K</m:r><m:r>) </m:r></span></i></m:oMath><![endif]--><!--[if !msEquation]--><!--[endif]--><span lang="EN-US">and the </span><span lang="EN">combination of density and heat capacity terms (pc)</span><span lang="EN"> were estimated as a demonstrative example.</span><span lang="EN-US">The sample (with known geometry) was subjected to electromagnetic radiation, generating a uniform and time constant volumetric heat flow within it. Real temperature profile was simulated adding white Gaussian noise to the original data, obtained from the theoretical model. For solving the objective function, simulated annealing and genetic algorithms, along with the traditional Levenberg-Marquardt method were used for comparative purposes. Results show similar findings of all algorithms for three simulation scenarios, as long as the signal to noise ratio sits at least at 30 dB. It means for practical purposes, that the estimation procedure presented here requires both, a good experimental design and an electronic instrumentation correctly specified.</span><span lang="EN-US">If both requirements are satisfied simultaneously, it is possible to estimate these type of parameters on-line, without need for an additional experimental setup.</span></p><p class="MsoNormal"><span lang="EN-US">This work considers the prediction in real time of physicochemical parameters of a sample heated in a uniform electromagnetic field. The thermal conductivity </span><!--[if gte msEquation 12]><m:oMath><i style='mso-bidi-font-style:normal'><span lang=EN-US style='font-family:"Cambria Math","serif"'><m:r>(</m:r><m:r>K</m:r><m:r>) </m:r></span></i></m:oMath><![endif]--><!--[if !msEquation]--><!--[endif]--><span lang="EN-US">and the </span><span lang="EN">combination of density and heat capacity terms (</span><!--[if gte msEquation 12]><m:oMath><i style='mso-bidi-font-style:normal'><span lang=EN style='font-family:"Cambria Math","serif"; mso-ansi-language:EN'><m:r>ρc</m:r><m:r>)</m:r></span></i></m:oMath><![endif]--><!--[if !msEquation]--><!--[endif]--><span lang="EN"> were estimated as a demonstrative example.</span><span lang="EN-US">The sample (with known geometry) was subjected to electromagnetic radiation, generating a uniform and time constant volumetric heat flow within it. Real temperature profile was simulated adding white Gaussian noise to the original data, obtained from the theoretical model. For solving the objective function, simulated annealing and genetic algorithms, along with the traditional Levenberg-Marquardt method were used for comparative purposes. Results show similar findings of all algorithms for three simulation scenarios, as long as the signal to noise ratio sits at least at 30 dB. It means for practical purposes, that the estimation procedure presented here requires both, a good experimental design and an electronic instrumentation correctly specified.</span><span lang="EN-US">If both requirements are satisfied simultaneously, it is possible to estimate these type of parameters on-line, without need for an additional experimental setup.</span></p>

A New Method for On-line Measurement of the Straightness Error of Machine Tools Using an Acceleration Sensor

2021 ◽  
Author(s):  
Kuo Liu ◽  
Yiming Cui ◽  
Zhisong Liu ◽  
Jiakun Wu ◽  
Yongqing Wang
Keyword(s):  
Machine Tools ◽  
Signal To Noise Ratio ◽  
Measurement Data ◽  
Geometric Error ◽  
Ar Model ◽  
High Signal ◽  
Line Measurement ◽  
On Line ◽  
Straightness Error ◽  
Zero Phase

Abstract In order to improve the poor efficiency in the measurement of the geometric error of machine tools’ linear axes, this paper has presented a method to measure and restructure the geometric error of linear axes that is based on accelerometers. This method takes advantage of the phenomenon that when acceleration is measured under different measuring speeds, different frequencies and amplitudes are produced. The measurement data of the high signal-to-noise ratio for various velocities was fused together and the straightness error of the measured axis was obtained by integrating the acceleration twice. In order to remove the trend terms error in the integration, a zero phase IIR Butterworth filter was designed, which guarantees the signal’s phase invariance after filtering. The data was continued with the AR model to eliminate the endpoints’ effect in the filtering. The proposed method was verified by numerical values and experiments. The results showed that the proposed method has better robustness, a wider bandwidth and a higher efficiency than the methods of measuring by laser interferometer. It is also able to measure the geometric error of linear axes with an accuracy that reaches the micron scale.

EMG SIGNAL DENOISING USING ADAPTIVE FILTERS THROUGH HYBRID OPTIMIZATION ALGORITHMS

2020 ◽  
pp. 2150009
Author(s):  
Bhattiprolu Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Optimization Algorithms ◽  
Maximum Error ◽  
Recursive Least Squares ◽  
Signal Denoising ◽  
Recording Equipment ◽  
Advantages And Disadvantages ◽  
Emg Signal ◽  
Signal Features

Electromyography (EMG) signal recording equipment is comparatively modern. Still, there are enough restrictions in detection, recording, and characterization of EMG signals because of nonlinearity in the equipment, which leads to noise components. The most commonly affecting artifacts are Power Line Interference (PLI-Noise), Baseline Wander noise (BW-Noise), and Electrocardiogram noise (ECG-Noise). Adaptive filters are advanced and effective solutions for EMG signal denoising, but the improper tuning of filter coefficients leads to noise components in the denoised EMG signal. This defect in adaptive filters triggers or motivates us to optimize the filter coefficients with existing meta-heuristics optimization algorithms. In this paper, Least Mean Squares (LMS) filter and Recursive Least Squares (RLS) adaptive filter coefficients are optimized with a new Hybrid Firefly–Particle Swarm Optimization (HFPSO) by taking the advantages and disadvantages of both the algorithms. Experiments are conducted with the proposed HFPSO and it proved better in EMG signal denoising in terms of the measured parameters like signal-to-noise ratio (SNR) in dB, maximum error (ME), mean square error (MSE), etc. In the second part of the work, the denoised EMG signal features are extracted for the diagnosis of diseases related to myopathy and neuropathy as EMG signal reflects the neuromuscular function and EMG signal examination may contribute to the diagnosis of muscle disorder linked to myopathy and neuropathy.

Nutrition management of growing pigs

2001 ◽  
Vol 28 ◽  
pp. 89-95
Author(s):  
C. T. Whittemore ◽  
D. M. Green ◽  
C. P. Schofield
Keyword(s):  
Production Efficiency ◽  
Prediction Models ◽  
Response Prediction ◽  
Growing Pigs ◽  
Nutritional Requirement ◽  
Response Measurement ◽  
Loop Control ◽  
On Line ◽  
Response To Change ◽  
Time Specific

AbstractNutritional management of pigs to optimise growth demands pig-specific, time-specific and place-specific determination and provision of nutritional requirement. These elements need to be incorporated into response prediction models that operate in a real-time (not retrospective) closed-loop control environment. This implies appropriate means for the on-line measurement of response to change in nutrient provision, and the simultaneous means for manipulation of feeding level and feed quality. The paper describes how response prediction modelling and response measurement may now be achieved. Optimisation may be pursued with mixed objectives, including those of production efficiency and environmental protection.

Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals

2020 ◽  
Vol 19 (03) ◽  
pp. 2050027
Author(s):  
Thandar Oo ◽  
Pornchai Phukpattaranont
Keyword(s):  
Signal To Noise Ratio ◽  
Training Data ◽  
Signal To Noise ◽  
Zero Crossing ◽  
Ecg Signals ◽  
Average Value ◽  
Emg Signal ◽  
Recognition Systems ◽  
Noise Ratio ◽  
Electromyography Signals

When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.

ROBOT-ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING GA PROCESS

2011 ◽  
Vol 11 (04) ◽  
pp. 827-843 ◽  
Author(s):  
S. PARASURAMAN ◽  
ARIF WICAKSONO OYONG
Keyword(s):  
Genetic Algorithm ◽  
Basic Principle ◽  
Stroke Rehabilitation ◽  
Real Life ◽  
Joint Torque ◽  
Robot Assisted ◽  
Emg Signal ◽  
Conversion Algorithm

This project focuses on the development of robot-assisted stroke rehabilitation by implementing electromyography (EMG) as the interface between robot and user communication. The key issue in the implementation of EMG in this application is the conversion of EMG signal into torque data. This article presents a methodology of EMG signal to estimated joint torque conversion by using genetic algorithm (GA). The basic principle of GA, formulation, and implementation to the problem are discussed in this article. Experimentation with real-life EMG data has been carried out to assess the feasibility of the methodology in robot-assisted stroke rehabilitation problem. Preliminary investigations show that the methodology can be used in EMG to joint torque conversion algorithm.

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