Please use this identifier to cite or link to this item:
|Title:||A wearable system for pre-impact fall detection|
|Authors:||Nyan, M.N. |
Body area network
|Source:||Nyan, M.N., Tay, F.E.H., Murugasu, E. (2008-12-05). A wearable system for pre-impact fall detection. Journal of Biomechanics 41 (16) : 3475-3481. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jbiomech.2008.08.009|
|Abstract:||Unique features of body segment kinematics in falls and activities of daily living (ADL) are applied to make automatic detection of a fall in its descending phase, prior to impact, possible. Fall-related injuries can thus be prevented or reduced by deploying fall impact reduction systems, such as an inflatable airbag for hip protection, before the impact. In this application, the authors propose the following hypothesis: "Thigh segments normally do not exceed a certain threshold angle to the side and forward directions in ADL, whereas this abnormal behavior occurs during a fall activity". Torso and thigh wearable inertial sensors (3D accelerometer and 2D gyroscope) are used and the whole system is based on a body area network (BAN) for the comfort of the wearer during a long term application. The hypothesis was validated in an experiment with 21 young healthy volunteers performing both normal ADL and fall activities. Results show that falls could be detected with an average lead-time of 700 ms before the impact occurs, with no false alarms (100% specificity), a sensitivity of 95.2%. This is the longest lead-time achieved so far in pre-impact fall detection. © 2008.|
|Source Title:||Journal of Biomechanics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 7, 2018
WEB OF SCIENCETM
checked on Feb 5, 2018
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.