The Experts below are selected from a list of 353883 Experts worldwide ranked by ideXlab platform

Larry S Davis - One of the best experts on this subject based on the ideXlab platform.

  • eigengait motion based recognition of people using image self similarity
    Lecture Notes in Computer Science, 2001
    Co-Authors: Chiraz Benabdelkader, Ross Cutler, Harsh Nanda, Larry S Davis
    Abstract:

    We present a novel Technique for motion-based recognition of individual gaits in monocular sequences. Recent work has suggested that the image self-similarity plot of a moving person/object is a projection of its planar dynamics. Hence we expect that these plots encode much information about gait motion patterns, and that they can serve as good discriminants between gaits of different people. We propose a method for gait recognition that uses similarity plots the same way that face images are used in eigenface-based face recognition Techniques. Specifically, we first apply Principal Component Analysis (PCA) to a set of training similarity plots, mapping them to a lower dimensional space that contains less unwanted variation and offers better separability of the data. Recognition of a new gait is then done via standard pattern classification of its corresponding similarity plot within this simpler space. We use the k-nearest neighbor rule and the Euclidian distance. We test this method on a data set of 40 sequences of six different walking subjects, at 30 FPS each.We use the leave-one-out Cross-Validation Technique to obtain an unbiased estimate of the recognition rate of 93%.

  • eigengait motion based recognition of people using image self similarity
    Lecture Notes in Computer Science, 2001
    Co-Authors: Chiraz Benabdelkader, Ross Cutler, Harsh Nanda, Larry S Davis
    Abstract:

    We present a novel Technique for motion-based recognition of individual gaits in monocular sequences. Recent work has suggested that the image self-similarity plot of a moving person/object is a projection of its planar dynamics. Hence we expect that these plots encode much information about gait motion patterns, and that they can serve as good discriminants between gaits of different people. We propose a method for gait recognition that uses similarity plots the same way that face images are used in eigenface-based face recognition Techniques. Specifically, we first apply Principal Component Analysis (PCA) to a set of training similarity plots, mapping them to a lower dimensional space that contains less unwanted variation and offers better separability of the data. Recognition of a new gait is then done via standard pattern classification of its corresponding similarity plot within this simpler space. We use the k-nearest neighbor rule and the Euclidian distance. We test this method on a data set of 40 sequences of six different walking subjects, at 30 FPS each.We use the leave-one-out Cross-Validation Technique to obtain an unbiased estimate of the recognition rate of 93%.

Ainara Garde - One of the best experts on this subject based on the ideXlab platform.

  • Breathing Pattern Characterization in Chronic Heart Failure Patients Using the Respiratory Flow Signal
    Annals of Biomedical Engineering, 2010
    Co-Authors: Ainara Garde, Raimon Jane, L. Sörnmo, Beatriz F. Giraldo
    Abstract:

    This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out Cross-Validation Technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.

  • Characterization of periodic and non-periodic breathing pattern in chronic heart failure patients
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2008
    Co-Authors: Ainara Garde, Beatriz F. Giraldo, Raimon Jane, Ivan Díaz, Sergio Herrera, Salvador Benito, Maite Domingo, Antonio Bayes-genis
    Abstract:

    Periodic breathing (PB) has a high prevalence in chronic heart failure (CHF) patients with mild to moderate symptoms and poor ventricular function. This work proposes the analysis and characterization of the respiratory pattern to identify periodic breathing pattern (PB) and non-periodic breathing pattern (nPB) through the respiratory flow signal. The respiratory pattern analysis is based on the extraction and the study of the flow envelope signal. The flow envelope signal is modelled by an autoregressive model (AR) whose coefficients would characterize the respiratory pattern of each group. The goodness of the characterization is evaluated through a linear and non linear classifier applied to the AR coefficients. An adaptive feature selection is used before the linear and non linear classification, employing leave-one-out cross validation Technique. With linear classification the percentage of well classified patients (8 PB and 18 nPB patients) is 84.6% using the statistically significant coefficients whereas with non linear classification, the percentage of well classified patients increase to more than 92% applying the best subset of coefficients extracted by a forward selection algorithm.

  • Analysis of Respiratory Flow Signals in Chronic Heart Failure Patients with Periodic Breathing
    2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
    Co-Authors: Ainara Garde, Raimon Jane, Ivan Díaz, Sergio Herrera, Salvador Benito, Maite Domingo, B. Giraldo, Antonio Bayes-genis
    Abstract:

    In patients with chronic heart failure (CHF), oscillatory breathing pattern predicts poor prognosis. This work proposes a method to identify the respiratory pattern to determine periodic breathing (PB), Cheyne-Stokes respiration (CSR) and non-periodic respiratory patterns (nPB) through the respiratory flow signal. 26 patients are studied, classified in G1 (PB), G2 (CSR) and G3 (nPB). The flow signal is filtered and normalized, to obtain the positive envelope that describes the respiratory pattern. With this new signal some features are extracted through its power spectral density (PSD). An adaptive feature selection algorithm is applied before the linear and non linear classification applying Leave-one-out Cross-Validation Technique. The result obtained with linear classification was 93% using the relation between total energy and frequency interval (ll), peak amplitude (ampp), peak frequency (fp), and the highest slope of the positive envelope's PSD (Slopemax). And the best result was obtained with non linear Technique, with 100% correctly classified patients, using only two parameters, fp and Slopemax.

Chiraz Benabdelkader - One of the best experts on this subject based on the ideXlab platform.

  • eigengait motion based recognition of people using image self similarity
    Lecture Notes in Computer Science, 2001
    Co-Authors: Chiraz Benabdelkader, Ross Cutler, Harsh Nanda, Larry S Davis
    Abstract:

    We present a novel Technique for motion-based recognition of individual gaits in monocular sequences. Recent work has suggested that the image self-similarity plot of a moving person/object is a projection of its planar dynamics. Hence we expect that these plots encode much information about gait motion patterns, and that they can serve as good discriminants between gaits of different people. We propose a method for gait recognition that uses similarity plots the same way that face images are used in eigenface-based face recognition Techniques. Specifically, we first apply Principal Component Analysis (PCA) to a set of training similarity plots, mapping them to a lower dimensional space that contains less unwanted variation and offers better separability of the data. Recognition of a new gait is then done via standard pattern classification of its corresponding similarity plot within this simpler space. We use the k-nearest neighbor rule and the Euclidian distance. We test this method on a data set of 40 sequences of six different walking subjects, at 30 FPS each.We use the leave-one-out Cross-Validation Technique to obtain an unbiased estimate of the recognition rate of 93%.

  • eigengait motion based recognition of people using image self similarity
    Lecture Notes in Computer Science, 2001
    Co-Authors: Chiraz Benabdelkader, Ross Cutler, Harsh Nanda, Larry S Davis
    Abstract:

    We present a novel Technique for motion-based recognition of individual gaits in monocular sequences. Recent work has suggested that the image self-similarity plot of a moving person/object is a projection of its planar dynamics. Hence we expect that these plots encode much information about gait motion patterns, and that they can serve as good discriminants between gaits of different people. We propose a method for gait recognition that uses similarity plots the same way that face images are used in eigenface-based face recognition Techniques. Specifically, we first apply Principal Component Analysis (PCA) to a set of training similarity plots, mapping them to a lower dimensional space that contains less unwanted variation and offers better separability of the data. Recognition of a new gait is then done via standard pattern classification of its corresponding similarity plot within this simpler space. We use the k-nearest neighbor rule and the Euclidian distance. We test this method on a data set of 40 sequences of six different walking subjects, at 30 FPS each.We use the leave-one-out Cross-Validation Technique to obtain an unbiased estimate of the recognition rate of 93%.

Antonio Bayes-genis - One of the best experts on this subject based on the ideXlab platform.

  • Characterization of periodic and non-periodic breathing pattern in chronic heart failure patients
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2008
    Co-Authors: Ainara Garde, Beatriz F. Giraldo, Raimon Jane, Ivan Díaz, Sergio Herrera, Salvador Benito, Maite Domingo, Antonio Bayes-genis
    Abstract:

    Periodic breathing (PB) has a high prevalence in chronic heart failure (CHF) patients with mild to moderate symptoms and poor ventricular function. This work proposes the analysis and characterization of the respiratory pattern to identify periodic breathing pattern (PB) and non-periodic breathing pattern (nPB) through the respiratory flow signal. The respiratory pattern analysis is based on the extraction and the study of the flow envelope signal. The flow envelope signal is modelled by an autoregressive model (AR) whose coefficients would characterize the respiratory pattern of each group. The goodness of the characterization is evaluated through a linear and non linear classifier applied to the AR coefficients. An adaptive feature selection is used before the linear and non linear classification, employing leave-one-out cross validation Technique. With linear classification the percentage of well classified patients (8 PB and 18 nPB patients) is 84.6% using the statistically significant coefficients whereas with non linear classification, the percentage of well classified patients increase to more than 92% applying the best subset of coefficients extracted by a forward selection algorithm.

  • Analysis of Respiratory Flow Signals in Chronic Heart Failure Patients with Periodic Breathing
    2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
    Co-Authors: Ainara Garde, Raimon Jane, Ivan Díaz, Sergio Herrera, Salvador Benito, Maite Domingo, B. Giraldo, Antonio Bayes-genis
    Abstract:

    In patients with chronic heart failure (CHF), oscillatory breathing pattern predicts poor prognosis. This work proposes a method to identify the respiratory pattern to determine periodic breathing (PB), Cheyne-Stokes respiration (CSR) and non-periodic respiratory patterns (nPB) through the respiratory flow signal. 26 patients are studied, classified in G1 (PB), G2 (CSR) and G3 (nPB). The flow signal is filtered and normalized, to obtain the positive envelope that describes the respiratory pattern. With this new signal some features are extracted through its power spectral density (PSD). An adaptive feature selection algorithm is applied before the linear and non linear classification applying Leave-one-out Cross-Validation Technique. The result obtained with linear classification was 93% using the relation between total energy and frequency interval (ll), peak amplitude (ampp), peak frequency (fp), and the highest slope of the positive envelope's PSD (Slopemax). And the best result was obtained with non linear Technique, with 100% correctly classified patients, using only two parameters, fp and Slopemax.

Beatriz F. Giraldo - One of the best experts on this subject based on the ideXlab platform.

  • Breathing Pattern Characterization in Chronic Heart Failure Patients Using the Respiratory Flow Signal
    Annals of Biomedical Engineering, 2010
    Co-Authors: Ainara Garde, Raimon Jane, L. Sörnmo, Beatriz F. Giraldo
    Abstract:

    This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out Cross-Validation Technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.

  • Characterization of periodic and non-periodic breathing pattern in chronic heart failure patients
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2008
    Co-Authors: Ainara Garde, Beatriz F. Giraldo, Raimon Jane, Ivan Díaz, Sergio Herrera, Salvador Benito, Maite Domingo, Antonio Bayes-genis
    Abstract:

    Periodic breathing (PB) has a high prevalence in chronic heart failure (CHF) patients with mild to moderate symptoms and poor ventricular function. This work proposes the analysis and characterization of the respiratory pattern to identify periodic breathing pattern (PB) and non-periodic breathing pattern (nPB) through the respiratory flow signal. The respiratory pattern analysis is based on the extraction and the study of the flow envelope signal. The flow envelope signal is modelled by an autoregressive model (AR) whose coefficients would characterize the respiratory pattern of each group. The goodness of the characterization is evaluated through a linear and non linear classifier applied to the AR coefficients. An adaptive feature selection is used before the linear and non linear classification, employing leave-one-out cross validation Technique. With linear classification the percentage of well classified patients (8 PB and 18 nPB patients) is 84.6% using the statistically significant coefficients whereas with non linear classification, the percentage of well classified patients increase to more than 92% applying the best subset of coefficients extracted by a forward selection algorithm.