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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’ fall- related injury goes to $43.8 billion by
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Development of a Fall Detecting System for the Elderly Residents
speaker: 林佑威
Author:Chia-Chi Wang, Chih-Yen Chiang, Po-Yen Lin, Yi-Chieh Chou, I-Ting Kuo, Chih-Ning Huang, Chia-Tai Chan
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on 1
OutlineI. IntroductionII. MethodIII. Experimental ResultsIV. 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’ fall-related 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.
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X軸
Y軸
Z軸
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 (x-z 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
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Flow of fall detection
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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