3D Accelerometer Presenter Chen Yu R0094049 Introduction 3D Accelerometer Applications about 3D accelerometers A RealTime Human Movement Classifier Analysis of Acceleration Signals using Wavelet ID: 625182
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Slide1
ADSP - Oral presentation3D Accelerometer
Presenter :
Chen Yu
R0094049Slide2
Introduction3D AccelerometerApplications about 3D accelerometers
A Real-Time Human Movement Classifier
Analysis of Acceleration Signals using Wavelet TransformActivity RecognitionConclusionReference
Outline
2Slide3
Introduction3D Accelerometer
Applications about 3D accelerometers
A Real-Time Human Movement ClassifierAnalysis of Acceleration Signals using Wavelet
TransformActivity
Recognition
ConclusionReference
Outline
3Slide4
Accelerometer is a device which can detect and measure acceleration.
Introduction
4Slide5
By measuring the vertical value of gravity, we can acquire the tilt angle of the accelerometer.
5
Introduction
the G value derived from the angle.Slide6
There are a lot of types of accelerometersCapacitive
Piezoelectric
PiezoresistiveHall EffectMagnetoresistive
Heat Transfer
Introduction
6Slide7
Introduction
7Slide8
Introduction3D Accelerometer
Applications about 3D accelerometers
A Real-Time Human Movement ClassifierAnalysis of Acceleration Signals using Wavelet
TransformActivity
Recognition
ConclusionReference
Outline
8Slide9
Basic Principle of AccelerationVelocity is speed and direction so any time there is a change in either speed or direction there is acceleration
.
Earth’s gravity: 1gBumps in road: 2gSpace shuttle: 10g
Death or serious injury: 50g
3D Accelerometer
9Slide10
Basic AccelerometerNewton’s lawHooke’s lawF =
kΔx
= ma 3D Accelerometer
10Slide11
Piezoelectric Systems3D Accelerometer
11Slide12
Electromechanical Systems
3D Accelerometer
12Slide13
Tilt angle3D Accelerometer
13Slide14
Introduction
3D Accelerometer
Applications about 3D accelerometersA Real-Time Human Movement Classifier
Analysis of Acceleration Signals using Wavelet
Transform
Activity Recognition
Conclusion
Reference
Outline
14Slide15
Calculate the user’s walking stateAnalyze the lameness of cattleDetect walking activity in
cardiac rehabilitation
Examine the gesture for cell phone or remote controller for video gamesApplications
about 3D accelerometers
15Slide16
Introduction3D Accelerometer
Applications
about 3D accelerometersA Real-Time Human Movement Classifier
Analysis of Acceleration Signals using Wavelet
Transform
Activity
Recognition
Conclusion
Reference
Outline
16Slide17
A Real-Time Human Movement Classifier
17Slide18
Human body’s movements are within frequency below 20 Hz (99% of the energy is contained below 15 Hz)Median filter
remove any abnormal noise spikes
Low pass filterGravitybodily motionA Real-Time Human Movement Classifier
18Slide19
A Real-Time Human Movement Classifier
Walk
Upstair
Downstair
19Slide20
Activity and RestAppropriate threshold value
Above the threshold -> active
Below the threshold -> rest
A Real-Time Human Movement Classifier
20Slide21
We define the Φ, which is the tilt angle between the positive z-axis and the gravitational vector
g
. we
can determine that a tilt angle between 20 and 60
is sitting, and angles of 0 to 20 standing, and the angle between 60 and 90 is lying.
21
A Real-Time Human Movement ClassifierSlide22
22
A Real-Time Human Movement ClassifierSlide23
When the patient is lying down, their orientation is divided into the categories of right side (
right
), left side (left), lying face down (
front), or lying on their back (
back
)
23
A Real-Time Human Movement ClassifierSlide24
Feature GenerationAverage:
Average acceleration (for each axis
)Standard Deviation
: Standard deviation (for each axis
)
Average Absolute Difference:
Average absolute difference
between the value of each of the
data
within the ED and the mean value over those
values
(for each axis
)
Average Resultant Acceleration
:
Average of the
square
roots of the sum of the values of each axis
squared over
the
ED
24
A Real-Time Human Movement
ClassifierSlide25
Time Between Peaks:
Time in milliseconds
between peaks in the sinusoidal waves associated with
most activities (for each axis
)
Binned Distribution:
We determine the range of values for
each axis (maximum – minimum), divide this range
into 10
equal sized bins, and then record what fraction of
the 200
values fell within each of the bins.
25
A Real-Time Human Movement ClassifierSlide26
Introduction
3D Accelerometer
Applications about 3D accelerometersA Real-Time Human Movement
ClassifierAnalysis of Acceleration Signals using Wavelet
Transform
Activity Recognition
Conclusion
Reference
Outline
26Slide27
Wavelet TransformAnalysis of Acceleration Signals using Wavelet Transform
g
[
n]
h
[
n]
2
2
g
[n]
h
[n]
2
x
LL
[
n
]
2
x
LH
[
n
]
g
[n]
h
[n]
2
2
x
H
L
[
n
]
x
HH
[
n
]
x
[
n
]
x
L
[
n
]
x
H
[
n
]
27Slide28
the original signal
x
[n
] can also be expanded by the mother wavelet function and the scaling function.
28
Analysis of Acceleration Signals using Wavelet TransformSlide29
Preprocessing :Windowing
The acceleration signals are accessed in real time in the system. Therefore, the system must cut a sequence of data into consecutive windows before data analysis.
Feature Selection
The advantage of the WT is that the wavelet coefficients imply the details in different bands.
29
Analysis of Acceleration Signals using Wavelet TransformSlide30
Power of maximum signal:Mean:
Variance:
Energy:The energy of neighbor difference:
30
Analysis of Acceleration Signals using Wavelet TransformSlide31
Introduction
3D Accelerometer
Applications about 3D accelerometersA Real-Time Human Movement ClassifierAnalysis of Acceleration Signals using Wavelet
TransformActivity Recognition
Conclusion
ReferenceOutline
31Slide32
There are several machine learning algorithms that can be used for classification, Gaussian mixture model (
GMM)
decision tree (J48) logistic regression
32
Activity RecognitionSlide33
Introduction
3D Accelerometer
Applications about 3D accelerometersA Real-Time Human Movement ClassifierAnalysis of Acceleration Signals using Wavelet
TransformActivity Recognition
Conclusion
Reference
Outline
33Slide34
34
Conclusion
Time analysis use decision tree
Time analysis use logistic regressionSlide35
Conclusion
35
The Wavelet transform use decision tree
The Wavelet transform use logistic regressionSlide36
Introduction
3D Accelerometer
Applications about 3D accelerometersA Real-Time Human Movement ClassifierAnalysis of Acceleration Signals using Wavelet
TransformActivity Recognition
Conclusion
Reference
Outline
36Slide37
P. Barralon
, N.
Vuillerme and N.
Noury, “Walk Detection With a Kinematic Sensor: Frequency and Wavelet Comparison,” IEEE EMBS Annual International Conference New York City, USA, Aug 30-Sept 3,
2006
M.
Sekine
, T. Tamura, M.
Akay
, T.
Togawa
, Y. Fukui, “Analysis of Acceleration Signals using Wavelet Transform,” Methods of Information in Medicine,
F. K. Schattauer Vrlagsgesellschaft mbH (2000
)
Elsa Garcia, Hang Ding and Antti Sarela
, “Can a mobile phone be used as a pedometer in an outpatient cardiac rehabilitation program?,” IEEE/ICME International Conference on Complex Medical Engineering July 13-15,2010, Gold Coast, Australia
Reference
37Slide38
Niranjan
Bidargaddi
, Antti
Sarela
,
Lasse
Klingbeil
and
Mohanraj
Karunanithi
, “Detecting walking activity in cardiac rehabilitation by using accelerometer
,”
Masaki
Sekine
,
Toshiyo
Tamura,
Metin
Akay
, Toshiro Fujimoto, Tatsuo
Togawa
, and Yasuhiro Fukui, “Discrimination of Walking Patterns Using Wavelet-Based Fractal Analysis,” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 10, NO. 3, SEPTEMBER
2002
“ Accelerometers and How they Work
”
“ Basic Principles of Operation and Applications of the Accelerometer ” Paschal Meehan and Keith
Moloney
- Limerick Institute of Technology.
Reference
38Slide39
From the lecture slide of “ Time Frequency Analysis and Wavelet Transform” by
Jian-Jiun
Ding
Jennifer R. Kwapisz
, Gary M. Weiss, Samuel A.
Moore “Activity Recognition using Cell Phone Accelerometers
”
Jian-Hua
Wang,
Jian-Jiun
Ding,
Yu
Chen
“
Automatic Gait recognition based on wavelet transform by using mobile phone
accelerometer
”
39
Reference