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THESIS REPORT
Behaviour recognition base on
smartphone's sensor
Student: Ngo Duy KienClass: K53CA
Supervisor: Bui The Duy
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p y
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Content
I. Falling detection problem
II. Principle and method for system
III. Research progressIV. Format of the thesis
V. Research plan
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FALLING DETECTIONproblem
33
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Personal EmergencyResponse (PER) System
44
1
Distinguishingbetween fallsand other types of normalbody
movement
2
Detecting a falland notifyingan emergencyserviceautomatically
3
Include acurrent locationfor cell phoneusing GPS or WIFI assisted
positioningsystem
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cce erome ersensor
- X-axis haspositive directiontoward the right side
of the device
- Y-axis haspositive direction
toward the top of the device
- Z-axis has
positive direction
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Principle and method for system
66
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Study of falls
77
q Falling Definition
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Study of falls
q Falling Types
88
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Study of falls
q Falling Types
99
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Study of non-falls
Non-falls
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Related Work
Real-Time Fall Detection based onShape and Motion Features
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Falling detection based onaccelerometer
q Analytical Method
§ Definition
§ Threshold
12
12
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Falling detection based onaccelerometer q Machine Learning
§ Identify natural clusters or groups of data contains
classified data and predicts the classes of unseen
data
§ Supervised Learning
§
Unsupervised Learning
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Machine Learning
Using a sliding window to transform thestream of acceleration data into instances for machine learning. The following attributeswere derived from the data within each 0.8second windows:
The average length of the acceleration vector within the window.
The average acceleration along the x, y,zaxes.
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RESEARCH PROGRESS
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Research progress
10/2011-11/2011:◦ Understood the problem
◦ Getting data and understood how
analysis data
12/2011- Now :
◦ Classification types of falls (oractivities daily living) with libsvm onweka
◦ 91%
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Format of the thesis
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Format of the thesis
1. Behavior problem and somepopular solution
2. Classification data with Machine
Learning
3. Implement system
4. Result and experiment
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Research plan
Finishing the classification withmachine learning
Compare the results with other
method to process data
Implement the system onSmartphone
Improving the implementation
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Thank you forlistening
2020