The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform

Levi J. Hargrove - One of the best experts on this subject based on the ideXlab platform.

  • the effects of electrode size and orientation on the sensitivity of myoelectric Pattern Recognition Systems to electrode shift
    IEEE Transactions on Biomedical Engineering, 2011
    Co-Authors: Aaron Young, Levi J. Hargrove, Todd A. Kuiken
    Abstract:

    Myoelectric Pattern Recognition Systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and the placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p <; 0.01). Another finding was the significant difference (p <; 0.01) in performance for the direction of electrode shift. Shifts perpendicular to the muscle fibers reduced classification accuracy and real-time controllability much more than shifts parallel to the muscle fibers. Increasing the size of the electrode detection surface was found to help reduce classification accuracy sensitivity to electrode shifts in a direction perpendicular to the muscle fibers but did not improve the real-time controllability of the Pattern Recognition system. One clinically important result was that a combination of longitudinal and transverse electrodes yielded high controllability with and without electrode shift using only four physical electrode pole locations.

  • The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift
    IEEE Transactions on Biomedical Engineering, 2011
    Co-Authors: Aaron J. Young, Levi J. Hargrove, Todd A. Kuiken
    Abstract:

    Myoelectric Pattern Recognition Systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and the placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p

  • Effects of interelectrode distance on the robustness of myoelectric Pattern Recognition Systems
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
    Co-Authors: Aaron J. Young, Levi J. Hargrove
    Abstract:

    Myoelectric Pattern Recognition control can potentially provide upper limb amputees with intuitive control of multiple prosthetic functions. However, the lack of robustness of myoelectric Pattern Recognition algorithms is a barrier for clinical implementation. One issue that can contribute to poor system performance is electrode shift, which is a change in the location of the electrodes with respect to the underlying muscles that occurs during donning and doffing and daily use. We investigated the effects of interelectrode distance and feature choice on system performance in the presence of electrode shift. Increasing the interelectrode distance from 2 cm to 4 cm significantly (p

Todd A. Kuiken - One of the best experts on this subject based on the ideXlab platform.

  • the effects of electrode size and orientation on the sensitivity of myoelectric Pattern Recognition Systems to electrode shift
    IEEE Transactions on Biomedical Engineering, 2011
    Co-Authors: Aaron Young, Levi J. Hargrove, Todd A. Kuiken
    Abstract:

    Myoelectric Pattern Recognition Systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and the placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p <; 0.01). Another finding was the significant difference (p <; 0.01) in performance for the direction of electrode shift. Shifts perpendicular to the muscle fibers reduced classification accuracy and real-time controllability much more than shifts parallel to the muscle fibers. Increasing the size of the electrode detection surface was found to help reduce classification accuracy sensitivity to electrode shifts in a direction perpendicular to the muscle fibers but did not improve the real-time controllability of the Pattern Recognition system. One clinically important result was that a combination of longitudinal and transverse electrodes yielded high controllability with and without electrode shift using only four physical electrode pole locations.

  • The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift
    IEEE Transactions on Biomedical Engineering, 2011
    Co-Authors: Aaron J. Young, Levi J. Hargrove, Todd A. Kuiken
    Abstract:

    Myoelectric Pattern Recognition Systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and the placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p

Patricia Melin - One of the best experts on this subject based on the ideXlab platform.

  • General type-2 fuzzy edge detectors applied to face Recognition Systems
    Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 2017
    Co-Authors: C.i. Gonzalez, Olivia Mendoza, J.r. Castro, Patricia Melin, Oscar Castillo
    Abstract:

    © 2016 IEEE. Edge detection is an essential step used in image processing Systems and can be applied to image sets before the training phase in Pattern Recognition Systems to improve performance. An edge detector simplifies the analysis of the images; because, it reduces the data to be processed by highlighting the most important features. In this paper we show the advantage of using a fuzzy edge detector method in a face Recognition system. In the proposed methodology, first the general type-2 fuzzy edge detector was applied over three image databases; secondly the Recognition system was implemented using a monolithic neural network, and after that the mean Recognition rate was obtained; finally the Recognition rate is compared to other edge detectors, such as the Sobel operator, Type-1 and Interval Type-2 fuzzy edge detectors.

  • General type-2 Fuzzy edge detector applied on face Recognition system using neural networks
    2016 IEEE International Conference on Fuzzy Systems FUZZ-IEEE 2016, 2016
    Co-Authors: C.i. Gonzalez, Olivia Mendoza, J.r. Castro, Patricia Melin
    Abstract:

    © 2016 IEEE. Edge detection is an essential method used in the image processing Systems and can be applied to image sets before the training phase in Pattern Recognition Systems. An edge detector simplifies the analysis of the images; because, it reduces the dataset processed. In this paper we present the advantage to use a fuzzy edge detector method in a face Recognition system. In the methodology, first the General type-2 fuzzy edge detector was applied over three image databases; secondly the Recognition system was performed using monolithic neural network, and after that the mean Recognition rate was obtained; finally the Recognition rate is compared using different edge detectors, such as the Sobel operator, Type-1 and Interval Type-2 fuzzy edge detectors.

Aaron J. Young - One of the best experts on this subject based on the ideXlab platform.

  • The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift
    IEEE Transactions on Biomedical Engineering, 2011
    Co-Authors: Aaron J. Young, Levi J. Hargrove, Todd A. Kuiken
    Abstract:

    Myoelectric Pattern Recognition Systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and the placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p

  • Effects of interelectrode distance on the robustness of myoelectric Pattern Recognition Systems
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
    Co-Authors: Aaron J. Young, Levi J. Hargrove
    Abstract:

    Myoelectric Pattern Recognition control can potentially provide upper limb amputees with intuitive control of multiple prosthetic functions. However, the lack of robustness of myoelectric Pattern Recognition algorithms is a barrier for clinical implementation. One issue that can contribute to poor system performance is electrode shift, which is a change in the location of the electrodes with respect to the underlying muscles that occurs during donning and doffing and daily use. We investigated the effects of interelectrode distance and feature choice on system performance in the presence of electrode shift. Increasing the interelectrode distance from 2 cm to 4 cm significantly (p

C.i. Gonzalez - One of the best experts on this subject based on the ideXlab platform.

  • General type-2 fuzzy edge detectors applied to face Recognition Systems
    Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 2017
    Co-Authors: C.i. Gonzalez, Olivia Mendoza, J.r. Castro, Patricia Melin, Oscar Castillo
    Abstract:

    © 2016 IEEE. Edge detection is an essential step used in image processing Systems and can be applied to image sets before the training phase in Pattern Recognition Systems to improve performance. An edge detector simplifies the analysis of the images; because, it reduces the data to be processed by highlighting the most important features. In this paper we show the advantage of using a fuzzy edge detector method in a face Recognition system. In the proposed methodology, first the general type-2 fuzzy edge detector was applied over three image databases; secondly the Recognition system was implemented using a monolithic neural network, and after that the mean Recognition rate was obtained; finally the Recognition rate is compared to other edge detectors, such as the Sobel operator, Type-1 and Interval Type-2 fuzzy edge detectors.

  • General type-2 Fuzzy edge detector applied on face Recognition system using neural networks
    2016 IEEE International Conference on Fuzzy Systems FUZZ-IEEE 2016, 2016
    Co-Authors: C.i. Gonzalez, Olivia Mendoza, J.r. Castro, Patricia Melin
    Abstract:

    © 2016 IEEE. Edge detection is an essential method used in the image processing Systems and can be applied to image sets before the training phase in Pattern Recognition Systems. An edge detector simplifies the analysis of the images; because, it reduces the dataset processed. In this paper we present the advantage to use a fuzzy edge detector method in a face Recognition system. In the methodology, first the General type-2 fuzzy edge detector was applied over three image databases; secondly the Recognition system was performed using monolithic neural network, and after that the mean Recognition rate was obtained; finally the Recognition rate is compared using different edge detectors, such as the Sobel operator, Type-1 and Interval Type-2 fuzzy edge detectors.