Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields
Joint Authors
Siddiqi, Muhammad Hameed
Alruwaili, Madallah
Ali, Amjad
Alanazi, Saad
Zeshan, Furkh
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-18
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Siddiqi, Muhammad Hameed& Alruwaili, Madallah& Ali, Amjad& Alanazi, Saad& Zeshan, Furkh. 2019. Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129632
Modern Language Association (MLA)
Siddiqi, Muhammad Hameed…[et al.]. Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1129632
American Medical Association (AMA)
Siddiqi, Muhammad Hameed& Alruwaili, Madallah& Ali, Amjad& Alanazi, Saad& Zeshan, Furkh. Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129632
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129632