National Changhua University of Education Institutional Repository : Item 987654321/11742

English | 正體中文 | 简体中文 | Items with full text/Total items : 6507/11669
Visitors : 30041516 Online Users : 537

RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope Adv. Search

Login ‧ Upload ‧ Help ‧ About ‧ Administer

NCUEIR > College of Engineering > Department and Graduate Institute of Electronic Engineering > Periodical Articles > Item 987654321/11742

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11742

Title:	Understanding of Human Behaviors from Videos in Nursing Care Monitoring System
Authors:	Liu, Chin-De;Chung, Pau-Choo;Chung, Yi-Nung;Thonnat, Monique
Contributors:	電機工程學系
Keywords:	Behavior analysis;Activity recognition;State machine;Scenario recognition
Date:	2007-02
Issue Date:	2012-07-02T02:06:36Z
Publisher:	IOS Press
Abstract:	This paper addresses the issue in scenario-based understanding of human behavior from videos in a nursing care monitoring system. The analysis is carried out based on experiments consisting of single-state scenarios and multi-state scenarios where the former monitors activities under contextual contexts for elementary behavior reasoning, while the latter dictating the elementary behavior order for behavior reasoning, with a priori knowledge in system profile for normality detection. By integrating the activities, situation context, and profile knowledge we can have a better understanding of patients in a monitoring system. In activity recognition, a Negation-Selection mechanism is developed. which employs a divide-and-conquer concept with the Negation using posture transition to preclude the negative set from the activities. The Selection that follows the Negation uses a moving history trace for activity recognition. Such a history trace composes not only the pose from single frame, but also history trajectory information. As a result, the activity can be more accurately identified. The developed approach has been established into a nursing care monitoring system for elder's daily life behaviors. Results have shown the promise of the approach which can accurately interpret 85% of the regular daily behavior. In addition, the approach is also applied to accident detection which was found to have 90% accuracy with 0% false alarm.
Relation:	Journal of High Speed Network, 16(1): 91-103
Appears in Collections:	[Department and Graduate Institute of Electronic Engineering] Periodical Articles

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	595	View/Open

Loading...