1. Presenter : Chen Yu R0094049

2.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 2

3.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 3

4.  Accelerometer is a device which can detect and measure acceleration. 4

5.  By measuring the vertical value of gravity, we can acquire the tilt angle of the accelerometer. 5 the G value derived from the angle.

6.  There are a lot of types of accelerometers ◦ Capacitive ◦ Piezoelectric ◦ Piezoresistive ◦ Hall Effect ◦ Magnetoresistive ◦ Heat Transfer 6

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8.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 8

9.  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 9

10.  Basic Accelerometer ◦ Newton’s law ◦ Hooke’s law ◦ F = kΔx = ma 10

11.  Piezoelectric Systems 11

12.  Electromechanical Systems 12

13.  Tilt angle 13

14.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 14

15.  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 15

16.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 16

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18.  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 18

19. Walk Upstair Downstair 19

20.  Activity and Rest ◦ Appropriate threshold value ◦ Above the threshold -> active ◦ Below the threshold -> rest 20

21.  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

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23.  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

24.  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 24

25. ◦ 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

26.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 26

27.  Wavelet Transform g[n]g[n] h[n]h[n]  2 g[n] h[n]  2 x LL [n]  2 x LH [n] g[n] h[n]  2 xHL[n]xHL[n] x HH [n] x[n]x[n] xL[n]xL[n] xH[n]xH[n] 27

28.  the original signal x[n] can also be expanded by the mother wavelet function and the scaling function. 28

29.  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

30.  Power of maximum signal:  Mean:  Variance:  Energy:  The energy of neighbor difference: 30

31.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 31

32.  There are several machine learning algorithms that can be used for classification,  Gaussian mixture model (GMM)  decision tree (J48)  logistic regression 32

33.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 33

34. 34 Time analysis use decision tree Time analysis use logistic regression

35. 35 The Wavelet transform use decision tree The Wavelet transform use logistic regression

36.  Introduction  3D Accelerometer  Applications about 3D accelerometers  A Real-Time Human Movement Classifier  Analysis of Acceleration Signals using Wavelet Transform  Activity Recognition  Conclusion  Reference 36

37.  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 37

38.  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. 38

39.  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