Most of the indoor accidents are related with fall down. Many medical studies are point out that key factor for keeping patient’s life is fast response of monitoring system. In modern life, peoples are isolated with neighbor, especially in living quarters. Therefore many solutions are developed for falling down monitoring that base on wearable sensors. These methods require of an expensive sensors system with electric power supplier and telecommunication devices. In context of patients with disease and weak status, patients are trend to remove sensor system. This issue requires to find out another approach so that sensors system will not be needed. We study the fall detection by monitoring camera. For increase the accuracy, we proposed a simple and effective method to extract features of abnormal activities. By tracking the magnitude of entropy and its distribution, our fall detection model has a capability of differentiating falls from other activities
Publication Information
Publisher
Thu Dau Mot University, Viet Nam
Editor-in-Chief
Assoc. Prof. Nguyen Van Hiep Thu Dau Mot University
Editorial Board
Assoc. Prof. Le Tuan Anh Thu Dau Mot University
PhD. Nguyen Quoc Cuong Thu Dau Mot University
PhD. Doan Ngoc Xuan Thu Dau Mot University
PhD. Nguyen Khoa Truong An Thu Dau Mot University
Assoc. Prof. Nguyen Thanh Binh Thu Dau Mot University
PhD. Le Thi Thuy Dung Thu Dau Mot University
PhD. Ngo Hong Diep Thu Dau Mot University
PhD. Nguyen Duc Dat Duc Ho Chi Minh City University of Industry and Trade
Assoc. Prof. Nguyen Van Duc Animal Husbandry Association of Vietnam
PhD. Nguyen Thi Nhat Hang Department of Education and Training of Binh Duong Province
PhD. Nguyen Thi Cam Le Vietnam Aviation Academy
PhD. Trần Hạnh Minh Phương Thu Dau Mot University
M.A. Pham Van Thinh Thu Dau Mot University
PhD. Nguyen Thi Lien Thuong Thu Dau Mot University