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Abstract:
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Fall are dangerous for the aged population as they can adversely affect heather.
Base on the importance and necessary of issue raised, recently, many being fall down
detection methods were developed aiming at monitoring the health of the elderly. Used
popular the most, device including a sensor and a microcontroller. In which, a sensor
recode data from accelerometer and using microcontroller to analysis, send alert when
fall occurs.
In this thesis, I focus to develop algorithms and the corresponding simulations to
monitor the falling event. It can discriminate between being fall down and the daily
activities of human such as standing, walking, sitting, lying. First, data were collected
from an accelerometer sensor which is highly exact and stable. The outputs of this sensor
are value of acceleration three axis measurements: ax, ay, az. This se nsor is put on the
belts, where can investigate exactly postures of human activities. Then, MATLAB
simulation software was used to combine with algorithms aiming at threshold analyzing
initial values. Being fall down are identified in the posture daily act ivities of human,
based on the sudden change of acceleration magnitude between the states . I also
investigated carefully the effects of choosing the threshold (to determine the falling event)
and the window size (to determine the time frame for analyzing). After that, we can select
the most suitable values for these parameters in order to achieve the best performance . |