Title :
A fall detection study based on neural network algorithm using AHRS
Author :
Qingbin Zhang ; Guohui Tian ; Nana Ding ; Yanru Zhang
Author_Institution :
Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China
Abstract :
Human fall detection devices with high recognition rate have an important significance for the elderly and patient to detect their falls which may lead to dangerous or even death. In this paper, attitude angle and tri-axial acceleration of the Attitude and Heading Reference System (AHRS) module on the waist was used for the fall detection system. A fall detection method based on neural network was presented which could accurately distinguish falls from activities of daily living (ADL) including walking, jumping, sitting, bending, squatting, lying down, etc. The experiment was carried out with different groups of objects. The experimental results demonstrated that the proposed method was efficient, reliable as well as practical.
Keywords :
geriatrics; image recognition; neural nets; ADL; AHRS module; Attitude and Heading Reference System; activities of daily living; attitude angle; elderly; fall detection method; fall detection system; high recognition rate; human fall detection devices; neural network algorithm; patient; triaxial acceleration; Acceleration; Accelerometers; Compass; Neural networks; Noise; Noise reduction; Wavelet transforms; AHRS module; Fall detection; fusion acceleration; neural network; sliding window;
Conference_Titel :
Information and Automation (ICIA), 2013 IEEE International Conference on
Conference_Location :
Yinchuan
DOI :
10.1109/ICInfA.2013.6720398