Feature extraction of forearm EMG signals for prosthetics

Rafiee, Javad; Rafiee, Mohammad A.; Yavari, Fazel; Schoen, Marco P.

doi:10.1016/j.eswa.2010.09.068

Cited by 148 publications

(77 citation statements)

References 36 publications

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“…[8] However, the difficulties in surface electromyogram signal classification for prosthetic applications lie in the selection of electrode locations on the arm, signal processing, and the extraction of a feature vector capable to classifying several motions. [9] The objectives of this study were to present the design of a multielectrode signal detection system: to validate the methods proposed for different choices of the processing parameters, to describe computer-assisted techniques, to interpret the different arm motions, to estimate the effects of surface electromyogram signal variations for different positions and motions, and to verify the validity of the results.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of Surface Electromyograms to Characterize Arm Movement

Veer

2014

Instrumentation Science & Technology

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& In this investigation, surface electromyogram signals were studied from muscles above the elbow to characterize operations of the arm. The instrumentation consisted of surface electrodes, signal acquisition protocols, and signal conditioning at different levels. A program code was written and used to acquire the signal from the hardware, and interpretation was done to estimate parameters using a simulated algorithm. Different types of arm operations were analyzed using one way repeated factorial analysis of variance to justify the use of the surface electromyogram signal to identify arm motions. The results also present operations that may be easily realized by prosthetic devices. This work provides fundamental information to develop more powerful, flexible, and efficient prosthetic design.

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Section: Introductionmentioning

confidence: 99%

Interpretation of Surface Electromyograms to Characterize Arm Movement

Veer

2014

Instrumentation Science & Technology

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“…Various signal filters have been applied and tested, most successfully with the Butterworth filters suggested by Fara et al [16]. Trigger identification methods have been explored including those discussed by Rafiee et al [22] and Staude et al [23].…”

Section: Hardware System Designmentioning

confidence: 99%

Rapid bicycle gear switching based on physiological cues

Hallett

Woodward

Schultz

et al. 2015

2015 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-This paper discusses the merits of Mechanomyography (MMG) sensors in capturing and isolating muscle activity in high interference environs, with application to 'hands free' gear shifting on a bicycle for users with limited extremity movement. MMG (acoustic) muscle sensing provides a simple and rugged alternative to physiological sensing for machine interface in the field, but suffers from interfering artifacts (in particular motion) which has limited its mainstream use. We introduce a system fusing MMG with a filter based on Inertial Measurement (IMU) to isolate muscle activity in the presence of interfering motion and vibrations. The system identifies user-initiated muscle trigger profiles during laboratory testing, allowing parameterization of MMG and IMU signals to identify purposeful muscle contractions (triggers) and to omit false triggers resulting from cycle/road vibration or rider movement. During laboratory testing the success rate of trigger identification was 88.5% while cycling with an average of 0.87 false triggers /min. During road testing the success rate was 72.5% and false triggers were more frequent at 3.7 /min. These results hold strong promise for alternative triggering mechanisms to the standard bar-end shifters used in current offthe-shelf cycling group sets, enabling amputees or people of reduced arm or hand dexterity to change gears while riding. Further testing will explore the use of signal filters on MMG data and further use of IMU data as feedback to increase false triggers rejection. Wider applications include a broad range of machineinteraction research.

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“…Especially, the time-frequency features, such as short time Fourier transform and wavelet transform, were complicated and high dimensional, so, the dimensionality reduction of the feature vector was required, and the classifier speed could be increased without loss of classification accuracy, and the principle component analysis (PCA) was utilized to accomplish this purpose [3,4]. And, the autoregressive (AR) coefficients [5] and mother wavelet matrix [6] could be adopted. Then, the pattern classification was employed.…”

Section: Introductionmentioning

confidence: 99%

Online Finger Gesture Recognition Using Surface Electromyography Signals

Li¹,

Li²

2013

JSIP

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The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed gestures with the extracted features. The experimental results showed that most of gestures could acquire the acceptable classification performance and a few elaborate gestures were hard to acquire the effective identification.

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Feature extraction of forearm EMG signals for prosthetics

Cited by 148 publications

References 36 publications

Interpretation of Surface Electromyograms to Characterize Arm Movement

Interpretation of Surface Electromyograms to Characterize Arm Movement

Rapid bicycle gear switching based on physiological cues

Online Finger Gesture Recognition Using Surface Electromyography Signals

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