Non-Linear Signal Processing Techniques Applied on EMG Signal for Muscle Fatigue Analysis During Dynamic Contraction

2012 ◽  
pp. 193-203
Author(s):  
Ram Kinker Mishra ◽  
Rina Maiti
Keyword(s):  
Signal Processing ◽  
Muscle Fatigue ◽  
Fatigue Analysis ◽  
Emg Signal ◽  
Dynamic Contraction ◽  
Non Linear ◽  
Linear Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems

2005 ◽  
Vol 85 (11) ◽  
pp. 2137-2160 ◽  
Author(s):  
Matthias Winterhalder ◽  
Björn Schelter ◽  
Wolfram Hesse ◽  
Karin Schwab ◽  
Lutz Leistritz ◽  
...  
Keyword(s):  
Signal Processing ◽  
Neural Systems ◽  
Linear Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

scholarly journals Discrimination of Parkinsonian Tremor From Essential Tremor by Voting Between Different EMG Signal Processing Techniques

2014 ◽  
Vol 10 (2) ◽  
pp. 11
Author(s):  
A Hossen ◽  
Z Al-Hakim ◽  
M Muthuraman ◽  
J Raethjen ◽  
G Deuschl ◽  
...  
Keyword(s):  
Signal Processing ◽  
Essential Tremor ◽  
Test Group ◽  
The Body ◽  
Soft Decision ◽  
Test Set ◽  
Parkinsonian Tremor ◽  
Emg Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

 Parkinson's disease (PD) and essential tremor (ET) are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor”. PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG) signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC) of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively. 

scholarly journals Pathological Electromyogram (EMG) Signal Analysis Parameters

2021 ◽  
Vol 4 (13) ◽  
pp. 01-14
Author(s):  
S. M. Debbal
Keyword(s):  
Signal Processing ◽  
Signal Analysis ◽  
Neuromuscular Diseases ◽  
Temporal Analysis ◽  
Clinical Analysis ◽  
Emg Signal ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Signal Treatment

Clinical analysis of the electromyogram is a powerful tool for diagnosis of neuromuscular diseases. There fore, the detection and the analysis of electromyogram signals has he attracted much attention over the years. Several methods based on modern signal Processing techniques such as temporal analysis, spectro-temporel analysis ..., have been investigated for electromyogram signal treatment. However, many of these analysis methods are not highly successful due to their complexity and non-stationarity. The aim of this study is to analyse the EMGs signals using nonlinear analysis. This analysis can provide a wide range of information’s related to the type of signal (normal and pathological).

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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