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ADSP - Oral presentation 3D Accelerometer. Presenter : Chen Yu R0094049. Outline. Introduction 3D Accelerometer Applications about 3D accelerometers A Real-Time Human Movement Classifier Analysis of Acceleration Signals using Wavelet Transform Activity Recognition Conclusion

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adsp oral presentation 3d accelerometer

ADSP - Oral presentation3D Accelerometer

Presenter : Chen Yu

R0094049

outline
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
outline1
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
introduction
Introduction
  • Accelerometer is a device which can detect and measure acceleration.
introduction1
Introduction
  • By measuring the vertical value of gravity, we can acquire the tilt angle of the accelerometer.

the G value derived from the angle.

introduction2
Introduction
  • There are a lot of types of accelerometers
    • Capacitive
    • Piezoelectric
    • Piezoresistive
    • Hall Effect
    • Magnetoresistive
    • Heat Transfer
outline2
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
3d accelerometer
3D Accelerometer
  • Basic Principle of Acceleration
    • Velocity is speed and direction so any time there is a change in either speed or direction there is acceleration.
    • Earth’s gravity: 1g
    • Bumps in road: 2g
    • Space shuttle: 10g
    • Death or serious injury: 50g
3d accelerometer1
3D Accelerometer
  • Basic Accelerometer
    • Newton’s law
    • Hooke’s law
    • F = kΔx = ma
3d accelerometer2
3D Accelerometer
  • Piezoelectric Systems
3d accelerometer3
3D Accelerometer
  • Electromechanical Systems
outline3
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
applications about 3d accelerometers
Applications about 3D accelerometers
  • Calculate the user’s walking state
  • Analyze the lameness of cattle
  • Detect walking activity in cardiac rehabilitation
  • Examine the gesture for cell phone or remote controller for video games
outline4
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
    • Analysis of Acceleration Signals using Wavelet Transform
    • Activity Recognition
  • Conclusion
  • Reference
a real time human movement classifier1
A Real-Time Human Movement Classifier
  • 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 filter
    • Gravity
    • bodily motion
a real time human movement classifier3
A Real-Time Human Movement Classifier
  • Activity and Rest
    • Appropriate threshold value
    • Above the threshold -> active
    • Below the threshold -> rest
a real time human movement classifier4
A Real-Time Human Movement Classifier
  • 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 60is sitting, and angles of 0 to 20 standing, and the angle between 60 and 90 is lying.
a real time human movement classifier6
A Real-Time Human Movement Classifier
  • 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)
a real time human movement classifier7
A Real-Time Human Movement Classifier
  • Feature Generation
    • Average: 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
a real time human movement classifier8
A Real-Time Human Movement Classifier
  • 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.
outline5
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
analysis of acceleration signals using wavelet transform
Analysis of Acceleration Signals using Wavelet Transform
  • Wavelet Transform

g[n]

 2

xLL[n]

g[n]

 2

xL[n]

h[n]

 2

xLH[n]

x[n]

xHL[n]

g[n]

 2

h[n]

 2

xH[n]

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xHH[n]

h[n]

analysis of acceleration signals using wavelet transform1
Analysis of Acceleration Signals using Wavelet Transform
  • the original signal x[n] can also be expanded by the mother wavelet function and the scaling function.
analysis of acceleration signals using wavelet transform2
Analysis of Acceleration Signals using Wavelet Transform
  • 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.
analysis of acceleration signals using wavelet transform3
Analysis of Acceleration Signals using Wavelet Transform
  • Power of maximum signal:
  • Mean:
  • Variance:
  • Energy:
  • The energy of neighbor difference:
outline6
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
activity recognition
Activity Recognition
  • There are several machine learning algorithms that can be used for classification,
  • Gaussian mixture model (GMM)
  • decision tree (J48)
  • logistic regression
outline7
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
conclusion
Conclusion

Time analysis use decision tree

Time analysis use logistic regression

conclusion1
Conclusion

The Wavelet transform use decision tree

The Wavelet transform use logistic regression

outline8
Outline
  • Introduction
  • 3D Accelerometer
  • Applications about 3D accelerometers
  • A Real-Time Human Movement Classifier
  • Analysis of Acceleration Signals using Wavelet Transform
  • Activity Recognition
  • Conclusion
  • Reference
reference
Reference
  • 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
reference1
Reference
  • NiranjanBidargaddi, AnttiSarela, LasseKlingbeil and MohanrajKarunanithi, “Detecting walking activity in cardiac rehabilitation by using accelerometer,”
  • Masaki Sekine, Toshiyo Tamura, MetinAkay, 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.
reference2
Reference
  • 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”