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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

wavelet time acceleration real time wavelet real acceleration human movement signals analysis accelerometer transform accelerometerapplications accelerometersa classifier classifieranalysis transformactivity recognitionconclusionreference outline introduction3d

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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