A Motor Point Identification Technique Based on Dempster Shafer Theory

2014 ◽  
Author(s):  
Madhavi Anugolu ◽  
Chandrasekhar Potluri ◽  
Alex Urfer ◽  
Marco P. Schoen
Keyword(s):  
Wavelet Transforms ◽  
Sampling Rate ◽  
Test Subject ◽  
Discrete Wavelet ◽  
Motor Point ◽  
Point Location ◽  
Semg Signal ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Semg Signals

The objective of this work is to identify the motor point location from the obtained sEMG signals using Dempster Shafer theory (DST). The proposed technique is applied on data obtained from a male test subject. In particular, the sEMG signals and its corresponding skeletal muscle force signals from the Flexor Digitorum Superficialis are acquired at a sampling rate of 2000 Hz using a Delsys Bangnoli- 16 EMG system. The acquired sEMG signals are rectified and filtered using a Discrete Wavelet Transforms (DWT) with a Daubechies 44 mother wavelet. For the system identification, an Output Error (OE) model structure is assumed to obtain the dynamic relation between the sEMG signal and the corresponding finger force signals. Subsequently, model based probabilities and fuzzy inference based probabilities are obtained for discrete sensor locations of a sEMG sensor array. Considering these evidences, a DST based motor point location identification method is proposed. The results based on one subject show the potential of the proposed theory and approach for affectively identifying motor point locations using an array sEMG sensor.

Development of a Surface EMG-Based Control System for Controlling Assistive Devices

2020 ◽  
pp. 765-785
Author(s):  
Uvanesh K. ◽  
Suraj Kumar Nayak ◽  
Biswajeet Champaty ◽  
Goutam Thakur ◽  
Biswajit Mohapatra ◽  
...  
Keyword(s):  
Control System ◽  
Wavelet Transforms ◽  
High Efficiency ◽  
Surface Emg ◽  
Assistive Device ◽  
Discrete Wavelet ◽  
Motor Disability ◽  
Robotic Vehicle ◽  
The Time Domain ◽  
Semg Signals

The current study discusses about the development of an EMG based wireless control system for the patients suffering from high-level motor disability. Surface EMG (sEMG) signals were processed in the time domain and using discrete wavelet transforms (DWT). The statistical features of the signals (sEMG, envelope of the squared sEMG and wavelet processed sEMG) were determined and analyzed. The analysis of the features suggested that the features of the envelope of the squared sEMG signals were sufficient to be used for high-efficiency classification and control signal generation. A hall-effect sensor based switching mechanism was introduced for controlling the duration of the activation of the device. The control signals were wirelessly transmitted to the assistive device (robotic vehicle). The training and the subsequent use of the developed control system were easy.

Improvement in GPR coherent noise attenuation using τ‐p and wavelet transforms

Geophysics ◽  
2004 ◽  
Vol 69 (3) ◽  
pp. 789-802 ◽  
Author(s):  
Luigia Nuzzo ◽  
Tatiana Quarta
Keyword(s):  
Wavelet Transforms ◽  
Sampling Rate ◽  
Window Size ◽  
Real Data ◽  
Discrete Wavelet ◽  
Noise Attenuation ◽  
Computationally Efficient ◽  
Coherent Noise ◽  
Amplitude Noise ◽  
Time Space

We present a new application of modern filtering techniques to ground‐penetrating radar (GPR) data processing for coherent noise attenuation. We compare the performance of the discrete wavelet transform (DWT) and the linear Radon transform (τ‐p) to classical time‐space and Fourier domain methods using a synthetic model and real data. The synthetic example simulates problems such as system ringing and surface scattering, which are common in real cases. The field examples illustrate the removal of nearly horizontal but variable‐amplitude noise features. In such situations, classical space‐domain techniques require several trials before finding an appropriate averaging window size. Our comparative analysis indicates that the DWT method is better suited for local filtering than are 2D frequency‐domain (f‐f) techniques, although the latter are computationally efficient. Radon‐based methods are slightly superior than the techniques previously used for local directional filtering, but they are slow and quite sensitive to the p‐sampling rate, p‐range, and sizes of the muting zone. Our results confirm that Radon and wavelet methods are effective in removing noise from GPR images with minimal distortions of the signal.

Development of a Surface EMG-Based Control System for Controlling Assistive Devices

2016 ◽  
pp. 335-355
Author(s):  
Uvanesh K. ◽  
Suraj Kumar Nayak ◽  
Biswajeet Champaty ◽  
Goutam Thakur ◽  
Biswajit Mohapatra ◽  
...  
Keyword(s):  
Control System ◽  
Wavelet Transforms ◽  
High Efficiency ◽  
Surface Emg ◽  
Assistive Device ◽  
Discrete Wavelet ◽  
Motor Disability ◽  
Robotic Vehicle ◽  
The Time Domain ◽  
Semg Signals

The current study discusses about the development of an EMG based wireless control system for the patients suffering from high-level motor disability. Surface EMG (sEMG) signals were processed in the time domain and using discrete wavelet transforms (DWT). The statistical features of the signals (sEMG, envelope of the squared sEMG and wavelet processed sEMG) were determined and analyzed. The analysis of the features suggested that the features of the envelope of the squared sEMG signals were sufficient to be used for high-efficiency classification and control signal generation. A hall-effect sensor based switching mechanism was introduced for controlling the duration of the activation of the device. The control signals were wirelessly transmitted to the assistive device (robotic vehicle). The training and the subsequent use of the developed control system were easy.

scholarly journals EMG Signal Based Pattern Recognition Of Grasping Movement Using MODWT And Weighted K- Nearest Neighbor

2019 ◽  
Vol 8 (10) ◽  
pp. 1759-1764
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Signal Processing Technique ◽  
Semg Signal ◽  
Emg Signal ◽  
Semg Signals

This study purposed and evaluates a method based on weighted K-NN classification of surface Electromyogram (sEMG) signals. The sEMG signal classification plays the key role in designing a prosthetic for amputee persons. Wavelet transform is new signal processing technique, which provides better resolution in time and frequency domain simultaneously. Due to these wavelet properties, it can be effectively used in processing the sEMG signal to determine certain amplitude changes at certain frequencies. This paper propose a Maximal overlap Discrete Wavelet Transform (MODWT) approach for Weighted K-NN classifier for classification of sEMG signals based Grasping movements. At level 5 signal decomposition using MODWT, useful resolution component of the sEMG signal is obtained. In this paper Time-domain (TD) features set is used, which shows a decent performance. In WKNN, use a square-inverse weighted technique to improve the performance of the K-NN. Hence, a novel feature set obtained from decomposed signal using MODWT is used to improve the performance of sEMG for classification. MODWT was used for de-noising and time scale feature extraction of sEMG signals. Several WKNN classifiers are tested to optimize classification accuracy and computational problems. PCA is use to reduce the size of the level 5 decomposed data. WKNN performance evaluation on K=10 values with or without PCA. Six hand grasping movements have been classified, results indicate that this method allows the classification of hand pattern recognition with high precision.

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

A New Method for Characterizing Replacement Rate Variation in Molecular Sequences: Application of the Fourier and Wavelet Models to Drosophila and Mammalian Proteins

Genetics ◽  
2000 ◽  
Vol 154 (1) ◽  
pp. 381-395
Author(s):  
Pavel Morozov ◽  
Tatyana Sitnikova ◽  
Gary Churchill ◽  
Francisco José Ayala ◽  
Andrey Rzhetsky
Keyword(s):  
Wavelet Transforms ◽  
Parametric Bootstrap ◽  
Real Data ◽  
New Method ◽  
Rate Variation ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Ratio Test ◽  
Replacement Rate ◽  
New Models

Abstract We propose models for describing replacement rate variation in genes and proteins, in which the profile of relative replacement rates along the length of a given sequence is defined as a function of the site number. We consider here two types of functions, one derived from the cosine Fourier series, and the other from discrete wavelet transforms. The number of parameters used for characterizing the substitution rates along the sequences can be flexibly changed and in their most parameter-rich versions, both Fourier and wavelet models become equivalent to the unrestricted-rates model, in which each site of a sequence alignment evolves at a unique rate. When applied to a few real data sets, the new models appeared to fit data better than the discrete gamma model when compared with the Akaike information criterion and the likelihood-ratio test, although the parametric bootstrap version of the Cox test performed for one of the data sets indicated that the difference in likelihoods between the two models is not significant. The new models are applicable to testing biological hypotheses such as the statistical identity of rate variation profiles among homologous protein families. These models are also useful for determining regions in genes and proteins that evolve significantly faster or slower than the sequence average. We illustrate the application of the new method by analyzing human immunoglobulin and Drosophilid alcohol dehydrogenase sequences.

scholarly journals Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3727
Author(s):  
Joel Dunham ◽  
Eric Johnson ◽  
Eric Feron ◽  
Brian German
Keyword(s):  
Sensor Fusion ◽  
A Priori ◽  
Evidential Reasoning ◽  
Unmanned Aerial Systems ◽  
Sufficient Information ◽  
User Input ◽  
Transition Potential ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Aerial Systems

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

An application of Dempster-Shafer theory to the assessment of biogas technology

Energy ◽  
1992 ◽  
Vol 17 (3) ◽  
pp. 205-214
Author(s):  
Aurora A. Kawahara ◽  
Peter M. Williams
Keyword(s):  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Biogas Technology
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