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Development of a Fall Detecting System
for the Elderly Residents
Author:
Chia-Chi Wang, Chih-Yen Chiang, Po-Yen Lin,
Yi-Chieh Chou, I-Ting Kuo, Chih-Ning Huang, Chia-Tai Chan
speaker: 林佑威
Bioinformatics and Biomedical Engineering, 2008. ICBBE
2008. The 2nd International Conference on
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Outline
I.
II.
III.
IV.
Introduction
Method
Experimental Results
Conclusion
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Introduction

25~35% of elderly residents experienced fall-related injury
more than one time per year.

30~40% of all needed to be hospitalized.

3% of the fallers helplessly lie without any external support for
more than 20 minutes

The cost forecasting of medical care for elderly residents’ fallrelated injury goes to $43.8 billion by 2020
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Research

(2003) Thomas Degen et al. inlaid two accelerometers into a
wrist watch

(2006) C.C. Yanget al. used a triple-axial accelerometer placed
at the waist level

(2005) U. Lindemann et al. proposed a pilot study with two
accelerometers into the hearing aid housing
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Method
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Sensor Position

Accelerometer has been used in various
studies to monitor a range of human movement

The paper Inlaid the accelerometers into the
hearing aid housing
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Four Criteria on Fall Detection

A accelerometer was placed above the ear side

The sample rate of the accelerometer was
200Hz.
Y軸
X軸
Z軸
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Four Criteria on Fall Detection

(1).Sum-vector of all axes (Sa): it is used to
describe the spatial variation of acceleration
during the falling interval.
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Four Criteria on Fall Detection

(2).Sum-vector of horizontal plane (Sh): An
acceleration change of the horizontal plane (xz plane)
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Four Criteria on Fall Detection
Timestamp of falling body to be at rest (Trs)
 Timestamp of the body’s initial contact to the
ground (Tic)

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Four Criteria on Fall Detection

Backward integration of reference velocity (Vmax)

According to the dynamics of free-falling objects, 0.2
meters height of potential energy completely
transformed into kinetic energy may give rise to a
velocity of 2 m/s.
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Four Criteria on Fall Detection
Flow of fall detection
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Experimental Results
1.Five volunteers
 2.Eight kinds of falling posture
 3.Seven daily activities

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Seven daily activities

The seven activities include standing, sitting
down, lying down, walking, jumping, going up
(down) stairs, and jogging
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Eight kinds of falling posture
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Falling:Right-Side to the Ground
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Lie Down Twice:Slow then Quick
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Experimnets
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Conclusion

These experimental results have demonstrated
the proposed falls detection is effective

The algorithm had been accomplished

The data need to be transmitted to the central
computer to do further data analysis
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Conclusion
The future work :

1.Bluetooth module

2.Alarm system with VoIP or SMS communications.
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