ADSP - Oral presentation 3D Accelerometer

1. ADSP - Oral presentation3D Accelerometer Presenter : Chen Yu R0094049

2. 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

3. 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

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

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

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

7. Introduction

8. 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

9. 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

10. 3D Accelerometer • Basic Accelerometer • Newton’s law • Hooke’s law • F = kΔx = ma

11. 3D Accelerometer • Piezoelectric Systems

12. 3D Accelerometer • Electromechanical Systems

13. 3D Accelerometer • Tilt angle

14. 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

15. 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

16. 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

17. A Real-Time Human Movement Classifier

18. 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

19. A Real-Time Human Movement Classifier Walk Upstair Downstair

20. A Real-Time Human Movement Classifier • Activity and Rest • Appropriate threshold value • Above the threshold -> active • Below the threshold -> rest

21. 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.

22. A Real-Time Human Movement Classifier

23. 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)

24. 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

25. 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.

26. 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

27. 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]  2 xHH[n] h[n]

28. 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.

29. 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.

30. Analysis of Acceleration Signals using Wavelet Transform • Power of maximum signal: • Mean: • Variance: • Energy: • The energy of neighbor difference:

31. 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

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

33. 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

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

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

36. 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

37. 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

38. 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.

39. 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”