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Computational Intelligence and Neuroscience
Volume 2019, Article ID 8590560, 14 pages
https://doi.org/10.1155/2019/8590560
Research Article

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

1College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
2Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

Correspondence should be addressed to Muhammad Hameed Siddiqi; moc.liamg@4891iqiddis

Received 12 May 2019; Accepted 10 July 2019; Published 18 August 2019

Academic Editor: Paolo Gastaldo

Copyright © 2019 Muhammad Hameed Siddiqi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 value ≤0.2 regarding the classification accuracy.