Redirigiendo al acceso original de articulo en 16 segundos...
ARTÍCULO
TITULO

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

Claudia Angelica Rivera-Romero    
Jorge Ulises Munoz-Minjares    
Carlos Lastre-Dominguez and Misael Lopez-Ramirez    

Resumen

Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient?s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.

 Artículos similares

       
 
Khrystyna Burshtynska, Iryna Zayats, Maksym Halochkin, Krzysztof Bakula and Lyubov Babiy    
This paper proposes a general methodological approach to hydrological modeling for determining the areas of flooded land in the plain part of the Dniester riverbed, the second largest river in Ukraine. The purpose of the study is the selection of paramet... ver más
Revista: Water

 
Mohaimenul Azam Khan Raiaan, Nur Mohammad Fahad, Shovan Chowdhury, Debopom Sutradhar, Saadman Sakib Mihad and Md. Motaharul Islam    
Significant threats to ecological equilibrium and sustainable agriculture are posed by the extinction of animal species and the subsequent effects on farms. Farmers face difficult decisions, such as installing electric fences to protect their farms, alth... ver más
Revista: Future Internet

 
Jianjun Chen, Zizhen Chen, Renjie Huang, Haotian You, Xiaowen Han, Tao Yue and Guoqing Zhou    
When employing remote sensing images, it is challenging to classify vegetation species and ground objects due to the abundance of wetland vegetation species and the high fragmentation of ground objects. Remote sensing images are classified primarily acco... ver más
Revista: Drones

 
Jianan Bai, Danyang Qin, Ping Zheng and Lin Ma    
In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause... ver más

 
Jianyuan Li, Xiaochun Lu, Ping Zhang and Qingquan Li    
The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual de... ver más
Revista: Water