Thesis Topic Details

Topic ID:
3096
Title:
FALL AND ACTIVITY MONITORING VIA TECHNOLOGY
Supervisor:
Sri Parameswaran
Research Area:
Embedded Systems, Biomedical Engineering
Associated Staff
Assessor:
Topic Details
Status:
Active
Type:
R & D
Programs:
CE
Group Suitable:
No
Industrial:
No
Pre-requisites:
--
Description:
A great deal of progress has been made in our understanding of the factors that
contribute to fall risk and in the development and validation of laboratory and clinical
tests of fall risk (eg the work done at POWMRI, some references here). However, to a
large extent, the success of this research often relies on self-report of falls in the realworld
outside of the laboratory or clinic as an outcome measure. While self report
through falls calendars, diaries or interview is widely used, it suffers from several
limitations: 1) self-report does not allow for objective quantification of the fall event,
2) obervation of study participants over a long period of time is required to obtain a
sufficient number of falls for meaningful analysis and evaluation, 3) recall of fall
events is highly subjective and relies significantly on both the motivation and memory
of the faller to report fall events, 4) in an ideal world, one would like to prevent a fall
from occurring in the first place or at least, identify any changes in fall risk over time.
In addition, changes in physical ability leading to increased risk of falls may occur
over time in a subtle, subjectively undetectable manner. Therefore it is important to
develop an objective and accurate assessment approach for monitoring physical
activity in everyday living environments such as the home.
Recent advances in microprocessor, sensor, storage, battery and wireless
communication technologies have driven a great deal of recent research for
continuous telemonitoring of physical activity. Lightweight, low-power inertial
sensors (linear accelerometers, gyroscopes and magnetometers) can be combined with
powerful microprocessors to provide highly precise quantification of physical activity
for either online (immediate) or offline processing. Such technology offers the
possibility to both acquire characteristic patterns of physical activity related to falls
(e.g. variability in the timing or asymmetry of the stride cycle) as well as provide an
alert signal if a fall occurs.
Comments:
--
Past Student Reports
  Thomas DAVIES in s2, 2010
FALL AND ACTIVITY MONITORING VIA TECHNOLOGY
 

Download report from the CSE Thesis Report Library

NOTE: only current CSE students can login to view and select reports to download.