Dataset Information

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields.

ABSTRACT: In healthcare, the analysis of patients' activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognition problem. Moreover, we contend that the existing HCRF model is inadequate by independence assumptions, which may reduce classification accuracy. Therefore, we utilized a new algorithm to relax the assumption, allowing our model to use full-covariance distribution. Also, in this work, we proved that computation wise our method has very much lower complexity against the existing methods. For the experiments, we used four publicly available standard datasets to show the performance. We utilized a 10-fold cross-validation scheme to train, assess, and compare the proposed model with the conditional learning method, hidden Markov model (HMM), and existing HCRF model which can only use diagonal-covariance Gaussian distributions. From the experiments, it is obvious that the proposed model showed a substantial improvement with p value ?0.2 regarding the classification accuracy.

SUBMITTER: Siddiqi MH

PROVIDER: S-EPMC6935449 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields.

Siddiqi Muhammad Hameed MH Alruwaili Madallah M Ali Amjad A Alanazi Saad S Zeshan Furkh F

Computational intelligence and neuroscience 20190818

In healthcare, the analysis of patients' activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognit ...[more]

PMID: 31915429

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Project description:Contouring a normal anatomical structure during radiation treatment planning requires significant time and effort. The authors present a fast and accurate semiautomatic contour delineation method to reduce the time and effort required of expert users.Following an initial segmentation on one CT slice, the user marks the target organ and nontarget pixels with a few simple brush strokes. The algorithm calculates statistics from this information that, in turn, determines the parameters of an energy function containing both boundary and regional components. The method uses a conditional random field graphical model to define the energy function to be minimized for obtaining an estimated optimal segmentation, and a graph partition algorithm to efficiently solve the energy function minimization. Organ boundary statistics are estimated from the segmentation and propagated to subsequent images; regional statistics are estimated from the simple brush strokes that are either propagated or redrawn as needed on subsequent images. This greatly reduces the user input needed and speeds up segmentations. The proposed method can be further accelerated with graph-based interpolation of alternating slices in place of user-guided segmentation. CT images from phantom and patients were used to evaluate this method. The authors determined the sensitivity and specificity of organ segmentations using physician-drawn contours as ground truth, as well as the predicted-to-ground truth surface distances. Finally, three physicians evaluated the contours for subjective acceptability. Interobserver and intraobserver analysis was also performed and Bland-Altman plots were used to evaluate agreement.Liver and kidney segmentations in patient volumetric CT images show that boundary samples provided on a single CT slice can be reused through the entire 3D stack of images to obtain accurate segmentation. In liver, our method has better sensitivity and specificity (0.925 and 0.995) than region growing (0.897 and 0.995) and level set methods (0.912 and 0.985) as well as shorter mean predicted-to-ground truth distance (2.13 mm) compared to regional growing (4.58 mm) and level set methods (8.55 mm and 4.74 mm). Similar results are observed in kidney segmentation. Physician evaluation of ten liver cases showed that 83% of contours did not need any modification, while 6% of contours needed modifications as assessed by two or more evaluators. In interobserver and intraobserver analysis, Bland-Altman plots showed our method to have better repeatability than the manual method while the delineation time was 15% faster on average.Our method achieves high accuracy in liver and kidney segmentation and considerably reduces the time and labor required for contour delineation. Since it extracts purely statistical information from the samples interactively specified by expert users, the method avoids heuristic assumptions commonly used by other methods. In addition, the method can be expanded to 3D directly without modification because the underlying graphical framework and graph partition optimization method fit naturally with the image grid structure.

Project description:BackgroundThe increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.ResultsWe benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.ConclusionWe extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.

Dataset Information

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields.

Publications

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields.

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