scholarly journals EMG Based Gesture Recognition Using the Unbiased Difference Power

2021 ◽  
Vol 11 (4) ◽  
pp. 1526
Author(s):  
Kimoon Kang ◽  
Hyun-Chool Shin
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Noise Power ◽  
Signal To Noise ◽  
Emg Signal ◽  
Power Equation ◽  
Average Accuracy ◽  
The Difference ◽  
Resting Period

In this paper, we propose an unbiased difference power that is robust against noise as a feature for electromyography (EMG)-based gesture recognition. The proposed unbiased difference power is obtained by subtracting the noise-biased part from the difference power. We derive the difference power equation and discover that the difference power is biased by twice the noise power. For noise power estimation, we utilized the characteristics of the EMG signal and estimated the noise power from the resting period. For performance evaluation, we used EMG signals provided by the open source Ninapro project database. We used the recognition accuracy as an evaluation index. We compare the recognition accuracy of the case using the proposed unbiased feature with those of two conventional cases. Experimental results show that the proposed unbiased difference power improves the accuracy compared with conventional ones. As the noise level increases, cases where the proposed unbiased difference power is used show a clear improvement in accuracy compared with the two conventional cases. For the signal-to-noise ratio (SNR) of 0 dB, the proposed unbiased difference power improves the average accuracy by more than 12%.

WAVELET-BASED DENOISING ALGORITHM FOR ROBUST EMG PATTERN RECOGNITION

2011 ◽  
Vol 10 (02) ◽  
pp. 157-167 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
PORNCHAI PHUKPATTARANONT ◽  
CHUSAK LIMSAKUL
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Hand Movements ◽  
Signal Recognition ◽  
Signal To Noise ◽  
Processing Stage ◽  
Emg Signal ◽  
Baseline System ◽  
Emg Pattern Recognition

A successful pre-processing stage based on wavelet denoising algorithm for electromyography (EMG) signal recognition is proposed. From the limitation of traditional universal wavelet denoising, the optimal weighted parameter is assigned for universal thresholding method. The optimal weight for increasing EMG recognition accuracy is 50–60% of traditional universal threshold with hard transformation. Experimental results show that it improved approximately from 2 to 50% of recognition accuracy for EMG with signal-to-noise ratio (SNR) in the range of 20 to 0 dB compared to a baseline system (without pre-processing stage) and traditional universal wavelet denoising. The results are evaluated through a large EMG dataset with seven kinds of hand movements and eight types of muscle positions.

Bias in estimates of mb at small magnitudes

1976 ◽  
Vol 66 (6) ◽  
pp. 1887-1904
Author(s):  
J. F. Evernden ◽  
W. M. Kohler
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
Systematic Bias ◽  
Station Correction ◽  
Signal To Noise ◽  
Identification Criterion ◽  
The Difference ◽  
Operational Networks ◽  
Worldwide Network ◽  
Simple Parameter

abstract A possibly significant factor in application of an identification criterion such as MS:mb is systematic bias in mb magnitude estimates at small magnitudes due to a variety of factors. Magnitude bias is the difference in magnitude value, positive or negative, between an observed network-based magnitude value and the expected magnitude value if all stations of the network had detected the event at high signal-to-noise ratio. This paper constitutes a partial study of the general problem; it evaluates the bias effects expected from both conceptual and operational networks when using parameters for noise and signal levels and standard deviations derived from observations, and when correcting observed station mb values solely via a simple parameter station correction factor. The analysis shows that any bias effects on mb inherent in any operational or potential worldwide network are so small as to have negligible effect on use of an MS:mb discriminant.

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.

Quantitation of Sulfamethazine in Pork Tissue by Thin-Layer Chromatography

1993 ◽  
Vol 76 (2) ◽  
pp. 335-341 ◽  
Author(s):  
Joseph Unruh ◽  
Daniel P Schwartz ◽  
Robert A Barford
Keyword(s):  
Thin Layer ◽  
Limit Of Detection ◽  
Solid Phase ◽  
Signal To Noise Ratio ◽  
Internal Standard ◽  
Signal To Noise ◽  
Thin Layer Chromatographic Plate ◽  
Average Accuracy ◽  
Chromatographic Plate ◽  
Layer Chromatography

Abstract Our earlier method to detect and quantitate sulfamethazine (SMZ) in milk at the 10 ppb level was modified to quantitate SMZ in pork tissue. Sulfabromomethazine (SBZ) is added to the tissue as an internal standard. SMZ and SBZ are extracted from the tissue into water as the supernatant of a centrifuged, aqueous homogenate and are cleaned up and concentrated by a series of solid-phase extractions. The sulfonamide-containing eluate is then separated on a silica gel thin-layer chromatographic plate. SBZ and SMZ are derivatized with fluorescamine, and their fluorescence is quantitated with a scanning densitometer. The limit of detection was estimated at 0.25 ppb (signal-to-noise ratio, 3:1). The average accuracy over the analysis range (0.54-21.8 ppb [μg/kg]) was 95.6% (standard deviation = 29.4%, n = 54).

The Noise Analysis of Fluid Systemsin Solid-State Nanopore Sensors

2013 ◽  
Vol 419 ◽  
pp. 517-520 ◽  
Author(s):  
Song Ying ◽  
Lei Wang ◽  
Wen Yuan Zhao
Keyword(s):  
Solid State ◽  
Signal To Noise Ratio ◽  
Noise Analysis ◽  
Power Spectra ◽  
Noise Power ◽  
Label Free ◽  
Signal To Noise ◽  
Fluid Systems ◽  
Noise Ratio ◽  
Nanopore Sensors

The solid-state nanopore sensor offers a versatile platform for the rapid, label-free electrical detection and analysis of single molecules, especially on DNA sequencing. However, the overall signal-to-noise ratio (SNA) is a major challenge in sequencing applications. In our work, two different fluid systems made by metal and plexiglass have been designed to improve the signal to noise ratio of the solid-state nanopore sensor. From the measurements on the noise power spectra with a variety of conditions, it is found that plexiglass fluid system coupled with shielding box produces a good quality of electric signals on nanopore sensors.

A NEW TECHNIQUE FOR THE STUDY OF SCATTER PROPAGATION IN THE TROPOSPHERE

1957 ◽  
Vol 35 (8) ◽  
pp. 823-830 ◽  
Author(s):  
J. H. Chapman ◽  
W. J. Heikkila ◽  
J. E. Hogarth
Keyword(s):  
Power Spectrum ◽  
Communication System ◽  
Carrier Frequency ◽  
Signal To Noise Ratio ◽  
Signal Strength ◽  
Noise Power ◽  
Signal To Noise ◽  
A New Technique ◽  
Conventional Signal ◽  
Noise Power Density

The power spectrum of the fluctuations in received signal strength on a near-optical U.H.F. circuit has been measured. The sidebands associated with these fluctuations can overlap the information-carrying sidebands of a communication system. When this happens, these sidebands must be taken into account in determining the signal-to-noise ratio of the system. In other words, the fluctuations then have the characteristics of noise, and therefore they are called propagation noise in the present paper. Experiments at a carrier frequency of 500 Mc. have shown that the propagation noise power density usually varies with sideband frequency ƒ (measured from the carrier) as 1/ƒ2, for f in the range 0.1 to 10 c.p.s. Departures from this law have been observed in the regions near 0.1 c.p.s. and 10 c.p.s. The measurement of the power spectrum directly offers several advantages over the conventional signal strength recording method, and these are discussed herein.

scholarly journals Design of an Indigenised Electronic Lock-In Amplifier

2000 ◽  
Vol 23 (1) ◽  
pp. 53-60
Author(s):  
Umesh Kumar
Keyword(s):  
Narrow Band ◽  
Signal To Noise Ratio ◽  
Broad Band ◽  
Power Line ◽  
Frequency Drift ◽  
Noise Power ◽  
Signal To Noise ◽  
Band Noise ◽  
Noise Ratio ◽  
Lock In

An indigenised lock-in amplifier is designed that enables the accurate measurement of signals contaminated by broad-band noise, power-line pick-up, frequency drift, or other sources of interference. It does this by means of an extremely narrow band detector which has the centre of its passband locked to the frequency of the signal to be measured. Large improvements in signal-to-noise ratio are achieved.

scholarly journals Novel Configurations of Ultrahigh Frequency (≤600 MHz) Analog Frontend for High Resolution Ultrasound Measurement

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2598
Author(s):  
Min Kim ◽  
Jinhyoung Park ◽  
Qifa Zhou ◽  
Koping Shung
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Ultrahigh Frequency ◽  
Ultrasound Measurement ◽  
Functional Responses ◽  
Signal To Noise ◽  
Coded Excitation ◽  
Noise Ratio ◽  
The Difference

In this article, an approach to designing and developing an ultrahigh frequency (≤600 MHz) ultrasound analog frontend with Golay coded excitation sequence for high resolution imaging applications is presented. For the purpose of visualizing specific structures or measuring functional responses of micron-sized biological samples, a higher frequency ultrasound is needed to obtain a decent spatial resolution while it lowers the signal-to-noise ratio, the difference in decibels between the signal level and the background noise level, due to the higher attenuation coefficient. In order to enhance the signal-to-noise ratio, conventional approach was to increase the transmit voltage level. However, it may cause damaging the extremely thin piezoelectric material in the ultrahigh frequency range. In this paper, we present a novel design of ultrahigh frequency (≤600 MHz) frontend system capable of performing pseudo Golay coded excitation by configuring four independently operating pulse generators in parallel and the consecutive delayed transmission from each channel. Compared with the conventional monocycle pulse approach, the signal-to-noise ratio of the proposed approach was improved by 7–9 dB without compromising the spatial resolution. The measured axial and lateral resolutions of wire targets were 16.4 µm and 10.6 µm by using 156 MHz 4 bit pseudo Golay coded excitation, respectively and 4.5 µm and 7.7 µm by using 312 MHz 4 bit pseudo Golay coded excitation, respectively.

scholarly journals Coherent Power Measurements with a Compact Airborne Ka-Band Precipitation Radar

2018 ◽  
Vol 35 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Andrew L. Pazmany ◽  
Samuel J. Haimov
Keyword(s):  
Signal To Noise Ratio ◽  
Power Estimation ◽  
Noise Power ◽  
Signal Power ◽  
Signal To Noise ◽  
Precipitation Radar ◽  
Noise Component ◽  
Ka Band ◽  
Noise Ratio ◽  
Conventional Signal

AbstractCoherent power is an alternative to the conventional noise-subtracted power technique for measuring weather radar signal power. The inherent noise-canceling feature of coherent power eliminates the need for estimating and subtracting the noise component, which is required when performing conventional signal power estimation at low signal-to-noise ratio. The coherent power technique is particularly useful when averaging a high number of samples to improve sensitivity to weak signals. In such cases, the signal power is small compared to the noise power and the required accuracy of the estimated noise power may be difficult to achieve. This paper compares conventional signal power estimation with the coherent power measurement technique by investigating bias, standard deviation, and probability of false alarm and detection rates as a function of signal-to-noise ratio and threshold level. This comparison is performed using analytical expressions, numerical simulations, and analysis of cloud and precipitation data collected with the airborne solid-state Ka-band precipitation radar (KPR) operated by the University of Wyoming.

scholarly journals Gesture recognition system based on CNN-IndRNN and OpenBCI

2021 ◽  
Vol 336 ◽  
pp. 06003
Author(s):  
Na Wu ◽  
Hao JIN ◽  
Xiachuan Pei ◽  
Shurong Dong ◽  
Jikui Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition System ◽  
Classification Performance ◽  
Non Invasive ◽  
Average Accuracy ◽  
Open Source Hardware ◽  
Semg Signals

Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.

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