Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields
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 b...
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Online Access: | http://dx.doi.org/10.1155/2019/8590560 |
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doaj-e1d32661acc945ed99bc059daa1f68612020-11-24T20:44:18ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732019-01-01201910.1155/2019/85905608590560Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random FieldsMuhammad Hameed Siddiqi0Madallah Alruwaili1Amjad Ali2Saad Alanazi3Furkh Zeshan4College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaCollege of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanCollege of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanIn 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.http://dx.doi.org/10.1155/2019/8590560 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Muhammad Hameed Siddiqi Madallah Alruwaili Amjad Ali Saad Alanazi Furkh Zeshan |
spellingShingle |
Muhammad Hameed Siddiqi Madallah Alruwaili Amjad Ali Saad Alanazi Furkh Zeshan Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields Computational Intelligence and Neuroscience |
author_facet |
Muhammad Hameed Siddiqi Madallah Alruwaili Amjad Ali Saad Alanazi Furkh Zeshan |
author_sort |
Muhammad Hameed Siddiqi |
title |
Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields |
title_short |
Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields |
title_full |
Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields |
title_fullStr |
Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields |
title_full_unstemmed |
Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields |
title_sort |
human activity recognition using gaussian mixture hidden conditional random fields |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2019-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2019/8590560 |
work_keys_str_mv |
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1716817760709771264 |