The Experts below are selected from a list of 53982 Experts worldwide ranked by ideXlab platform

Amir Pourabdollah - One of the best experts on this subject based on the ideXlab platform.

  • Eating and drinking gesture spotting and recognition using a novel adaptive Segmentation Technique and a gesture discrepancy measure
    Expert Systems with Applications, 2020
    Co-Authors: Dario Ortega Anderez, Ahmad Lotfi, Amir Pourabdollah
    Abstract:

    Abstract Despite the increasing developments on human activity recognition using wearable technology, there are still many open challenges in spotting and recognising sporadic gestures. As opposed to activities, which exhibit continuous behaviour, the difficulty of spotting gestures lies in their rather sparse nature. This paper proposes a novel solution to spot and recognise a set of similar eating and drinking gestures from continuous inertial data streams. First, potential segments containing an eating or a drinking gesture are found using a Crossings-based Adaptive Segmentation Technique (CAST). Second, further to the long-established range of features employed in previous human activities recognition research work, a gesture discrepancy measure is proposed to improve the classification performance of the system. At the final step, a range of state-of-the-art classification models is employed for evaluation. Various conclusions can be drawn from the results obtained. First, given the 100% recall achieved at the Segmentation step, the CAST can be considered a reliable Segmentation Technique for spotting drinking and eating gestures which may be employed in future gesture spotting work. Second, the addition of gesture discrepancy as a feature descriptor consistently improves the classification performance of the system. Third, the reliability of the food and drink intake monitoring approach proposed in this work finds support on the out-performance of previous similar work.

  • temporal convolution neural network for food and drink intake recognition
    Pervasive Technologies Related to Assistive Environments, 2019
    Co-Authors: Dario Ortega Anderez, Ahmad Lotfi, Amir Pourabdollah
    Abstract:

    Eating difficulties are a prevalent issue within the elderly population, leading to weight loss and malnutrition. Likewise a poor diet is considered a confounding factor for developing chronic diseases and functional limitations. Given the above issues, alongside the current advances in computational intelligence achieved with the use Convolutional Neural Networks (CNNs), this paper proposes a wrist-worn tri-axial accelerometer-based food and drink intake monitoring system by combining an adaptive Segmentation Technique and a CNN model using 1-dimensional (1D) temporal convolutions. First, potential eating or drinking gestures are identified by the use of the adaptive Segmentation Technique. Once identified, the resultant gesture set is used to train the network for the recognition of four commonly occurring dietary gestures (drinking, using a spoon, using a fork and using the hand to take a bite). The problem is tackled as a 5-class classification model where the remaining class is composed by all the irrelevant gestures. The results reported, with an average per-class classification accuracy of 97.15%, suggest the system proposed is a viable solution for food and drink intake monitoring as well as a great contribution to the field of pervasive computing in support of independent living.

  • PETRA - Temporal convolution neural network for food and drink intake recognition
    Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2019
    Co-Authors: Dario Ortega Anderez, Ahmad Lotfi, Amir Pourabdollah
    Abstract:

    Eating difficulties are a prevalent issue within the elderly population, leading to weight loss and malnutrition. Likewise a poor diet is considered a confounding factor for developing chronic diseases and functional limitations. Given the above issues, alongside the current advances in computational intelligence achieved with the use Convolutional Neural Networks (CNNs), this paper proposes a wrist-worn tri-axial accelerometer-based food and drink intake monitoring system by combining an adaptive Segmentation Technique and a CNN model using 1-dimensional (1D) temporal convolutions. First, potential eating or drinking gestures are identified by the use of the adaptive Segmentation Technique. Once identified, the resultant gesture set is used to train the network for the recognition of four commonly occurring dietary gestures (drinking, using a spoon, using a fork and using the hand to take a bite). The problem is tackled as a 5-class classification model where the remaining class is composed by all the irrelevant gestures. The results reported, with an average per-class classification accuracy of 97.15%, suggest the system proposed is a viable solution for food and drink intake monitoring as well as a great contribution to the field of pervasive computing in support of independent living.

Alan Connelly - One of the best experts on this subject based on the ideXlab platform.

  • A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)
    Neuroinformatics, 2016
    Co-Authors: Alan Connelly, Jeanie L. Y. Cheong, Alicia J. Spittle, Jian Chen, Christopher Adamson, Zohra M. Ahmadzai, Sandra Rees, Lex W. Doyle, Peter J. Anderson, Deanne K. Thompson
    Abstract:

    Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated Segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain’s Integrated Registration and Segmentation Tool) and FreeSurfer. These Segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific Segmentation Technique will produce more accurate Segmentation results. In this study, we describe a new automated Segmentation Technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized Segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The Segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual Segmentation as the ground truth, utilizing spatial overlap (Dice’s coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90 % and ICC scores ≥0.77 when compared with manual Segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap ( p _ FDR  

  • A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)
    Neuroinformatics, 2015
    Co-Authors: Wai Yen Loh, Alan Connelly, Jeanie L. Y. Cheong, Alicia J. Spittle, Jian Chen, Zohra M. Ahmadzai, Sandra Rees, Chris Adamson, Lillian Gabra Fam, Katherine J. Lee
    Abstract:

    Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated Segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain’s Integrated Registration and Segmentation Tool) and FreeSurfer. These Segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific Segmentation Technique will produce more accurate Segmentation results. In this study, we describe a new automated Segmentation Technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized Segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The Segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual Segmentation as the ground truth, utilizing spatial overlap (Dice’s coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90 % and ICC scores ≥0.77 when compared with manual Segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap (p FDR < 0.05) and ICC scores, with less volumetric bias according to Bland-Altman plots. PSST is a customized Segmentation pipeline with an age-specific atlas that accurately segments typical and atypical basal ganglia and thalami at age 7 years, and has the potential to be applied to other pediatric datasets.

Deanne K. Thompson - One of the best experts on this subject based on the ideXlab platform.

  • A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)
    Neuroinformatics, 2016
    Co-Authors: Alan Connelly, Jeanie L. Y. Cheong, Alicia J. Spittle, Jian Chen, Christopher Adamson, Zohra M. Ahmadzai, Sandra Rees, Lex W. Doyle, Peter J. Anderson, Deanne K. Thompson
    Abstract:

    Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated Segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain’s Integrated Registration and Segmentation Tool) and FreeSurfer. These Segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific Segmentation Technique will produce more accurate Segmentation results. In this study, we describe a new automated Segmentation Technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized Segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The Segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual Segmentation as the ground truth, utilizing spatial overlap (Dice’s coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90 % and ICC scores ≥0.77 when compared with manual Segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap ( p _ FDR  

Katherine J. Lee - One of the best experts on this subject based on the ideXlab platform.

  • A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)
    Neuroinformatics, 2015
    Co-Authors: Wai Yen Loh, Alan Connelly, Jeanie L. Y. Cheong, Alicia J. Spittle, Jian Chen, Zohra M. Ahmadzai, Sandra Rees, Chris Adamson, Lillian Gabra Fam, Katherine J. Lee
    Abstract:

    Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated Segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain’s Integrated Registration and Segmentation Tool) and FreeSurfer. These Segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific Segmentation Technique will produce more accurate Segmentation results. In this study, we describe a new automated Segmentation Technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized Segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The Segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual Segmentation as the ground truth, utilizing spatial overlap (Dice’s coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90 % and ICC scores ≥0.77 when compared with manual Segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap (p FDR < 0.05) and ICC scores, with less volumetric bias according to Bland-Altman plots. PSST is a customized Segmentation pipeline with an age-specific atlas that accurately segments typical and atypical basal ganglia and thalami at age 7 years, and has the potential to be applied to other pediatric datasets.

Jeanie L. Y. Cheong - One of the best experts on this subject based on the ideXlab platform.

  • A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)
    Neuroinformatics, 2016
    Co-Authors: Alan Connelly, Jeanie L. Y. Cheong, Alicia J. Spittle, Jian Chen, Christopher Adamson, Zohra M. Ahmadzai, Sandra Rees, Lex W. Doyle, Peter J. Anderson, Deanne K. Thompson
    Abstract:

    Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated Segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain’s Integrated Registration and Segmentation Tool) and FreeSurfer. These Segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific Segmentation Technique will produce more accurate Segmentation results. In this study, we describe a new automated Segmentation Technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized Segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The Segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual Segmentation as the ground truth, utilizing spatial overlap (Dice’s coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90 % and ICC scores ≥0.77 when compared with manual Segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap ( p _ FDR  

  • A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)
    Neuroinformatics, 2015
    Co-Authors: Wai Yen Loh, Alan Connelly, Jeanie L. Y. Cheong, Alicia J. Spittle, Jian Chen, Zohra M. Ahmadzai, Sandra Rees, Chris Adamson, Lillian Gabra Fam, Katherine J. Lee
    Abstract:

    Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated Segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain’s Integrated Registration and Segmentation Tool) and FreeSurfer. These Segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific Segmentation Technique will produce more accurate Segmentation results. In this study, we describe a new automated Segmentation Technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized Segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The Segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual Segmentation as the ground truth, utilizing spatial overlap (Dice’s coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90 % and ICC scores ≥0.77 when compared with manual Segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap (p FDR < 0.05) and ICC scores, with less volumetric bias according to Bland-Altman plots. PSST is a customized Segmentation pipeline with an age-specific atlas that accurately segments typical and atypical basal ganglia and thalami at age 7 years, and has the potential to be applied to other pediatric datasets.