Falls often cause serious injury and health threats for elderly people. It is also the major obstacle to independent living for frail and elderly people. Many researchers try to establish an efficient fall prevention strategies for elderly people by collecting a lot of fall characteristics. However, it is difficult to obtain these characteristics simply from the questionnaires of elderly people. Since they may forget or misremember their falling scenario. In this work, we propose a fall characteristics collection system for designing fall prevention strategies. A waist-mounted tri-axial accelerometer is used to capture the movement data of the human body when elderly people fall. Then, the proposed algorithm uses the variations of angle between acceleration vector and three axes to determine the fall characteristics which include falling directions and impact parts. Experimental results demonstrate effectiveness of the proposed scheme. The system is not only cost effective but also portable that fulfills the requirements of fall characteristics data collection.
[1]
D. Oliver,et al.
Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies
,
1997,
BMJ.
[2]
Kamiar Aminian,et al.
Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly
,
2002,
IEEE Transactions on Biomedical Engineering.
[3]
S. Felder,et al.
Ageing of population and health care expenditure: a red herring?
,
1999,
Health economics.
[4]
Chia-Tai Chan,et al.
A Reliable Fall Detection System Based on Wearable Sensor and Signal Magnitude Area for Elderly Residents
,
2010,
ICOST.