Earticle

초록

영어

A factor seriously endangers the people health is falling, particularly for patients and the elderly. Fall detection systems contribute in preventing the consequences of the late medical aid and injuries endangering the people health. The main problem within fall detection systems is how to correctly distinguish between a fall and the other daily activities. There are various types of fall detection systems each of which has different advantages and disadvantages. Wireless motion-sensor based systems such as accelerometer and gyroscope provide higher efficiency with lower limits. This study introduces a new fall detection method employing motion sensors in smart phones to collect data due to the ease of access and application. To provide high efficiency for people with various ages and conditions, this method also takes advantages of adaptive-fuzzy neural networks for learning and inference. These methods correctly detect all 4 types of fall from 9 main daily activity groups.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Proposed Method
 4. Implementation
 5. Evaluating of the Proposed Method
 6. Conclusion
 7. Future Works
 Acknowledgment
 References

키워드

저자

• Fardin Abdali-Mohammadi [ Department of Computer Engineering and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran ]
• Mino Rashidpour [ Department of Computer Engineering and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran ]
• Abdolhossein Fathi [ Department of Computer Engineering and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605