Share Email Print
cover

Proceedings Paper

A method of posture monitoring and falling detection based on physiological and behavioral characteristics of the elderly
Author(s): Shiyun Zhou; Zhijia Yang; Yuxuan Mao; Haisong Tang; Yanchen Liu; Xiaozheng Liu; Liquan Dong
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

At present, the problem of population aging has become a hot spot of international concern, especially in China, and the international community urgently needs a universally applicable health care system for the elderly. Recent research shows that falling is the biggest threat to the health of the elderly. Based on thihe physiological and behavioral characteristics of the elderly, the paper discusses an algorithm for the recognition of motion state and fall detection of elderly applied to wearable devices to ensure timely rescue after a fall. The algorithm continuously acquires acceleration information during the movement of the elderly through a six-axis acceleration sensor. Firstly, the acceleration data is filtered, then the combined acceleration is calculated, and multiple features of the continuous data are extracted, and then the softmax method is used to classify the different motion states to realize the alarm of the fall. The algorithm extracts the feature vector by the magnitude of the combined acceleration, which solves the problem that the single acceleration in the traditional algorithm must solves the coordinate axis, which may waste much calculating time. The algorithm is validated by using the existing data set, and the accuracy of the algorithm is up to 89%. It is an effective way to detect falls.

Paper Details

Date Published: 12 March 2020
PDF: 13 pages
Proc. SPIE 11434, 2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments, 114341I (12 March 2020); doi: 10.1117/12.2550263
Show Author Affiliations
Shiyun Zhou, Beijing Institute of Technology (China)
Zhijia Yang, Beijing Institute of Technology (China)
Yuxuan Mao, Beijing Institute of Technology (China)
Haisong Tang, Beijing Institute of Technology (China)
Yanchen Liu, Beijing Institute of Technology (China)
Xiaozheng Liu, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11434:
2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments
Juan Liu; Baohua Jia; Xincheng Yao; Yongtian Wang; Takanori Nomura, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray