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...
Main Authors: | Muhammad Hameed Siddiqi, Madallah Alruwaili, Amjad Ali, Saad Alanazi, Furkh Zeshan |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2019-01-01
|
Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2019/8590560 |
Similar Items
-
An improved gaussian mixture hidden conditional random fields model for audio-based emotions classification
by: Muhammad Hameed Siddiqi
Published: (2021-03-01) -
A Novel Feature Selection Method for Video-Based Human Activity Recognition Systems
by: Muhammad Hameed Siddiqi, et al.
Published: (2019-01-01) -
Human Body Segmentation Using Level Set-Based Active Contours With Application on Activity Recognition
by: Madallah Alruwaili, et al.
Published: (2019-01-01) -
A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera
by: Muhammad Hameed Siddiqi, et al.
Published: (2021-01-01) -
Wheel Loader Driving Intention Recognition with Gaussian Mixture - Hidden Markov Model
by: Cao Guoxiang, et al.
Published: (2018-01-01)