1. Posture Monitoring System for Context Awareness in Mobile Computing
Authors:Jonghun Baek and Byoung-Ju Yun
Adviser: Yu-Chiang Li
Speaker: Gung-Shian Lin
Date:2011/01/14
IEEE Transactions on Instrumentation and Measurement, VOL. 59, NO. 6, JUNE 2010

2. User Posture Monitoring
Outline 1
Introduction 2
Sensors 3
TAMA 4
User Posture Monitoring 5
Recognition results 6
Conclusion

3. 1. Introduction
The posture of a user is one of the contextual information that can be used for mobile applications and the treatment of idiopathic scoliosis.
This paper describes a method for monitoring the posture of a user during operation of a mobile device in three activities such as sitting, standing, and walking.

4. 1. Introduction
The user posture monitoring system (UPMS) proposed in this paper is based on two major technologies.
The first involves a tilt-angle measurement algorithm (TAMA) using an accelerometer.
The second technology is an effective signal-processing method that eliminates the motion acceleration component of the accelerometer signal using a second-order Butterworth low-pass filter (SLPF).

5. Typical output values of the accelerometer due to gravity.
2. Sensors
Typical output values of the accelerometer due to gravity.

6. 3. TAMA
It used the reference vectors defined as the acceleration values measured at 0◦ of the X- and Y -axes compensated at the datum angle, respectively.

7. 3. TAMA
Signal Processing for Measuring the Tilt Angle

8. 3. TAMA Data Collection Method
The time-series acceleration data from the accelerometer was gathered for approximately 30 s for each degree at a sampling rate of the 100 samples/s, and it is termed the training data set.

9. 3. TAMA Compensation and Reference Vectors
We define the offset errors and the reference vectors as the model parameters of the TAMA.

10. 3. TAMA
The equations for the model parameters and compensation for each axis in each datum angle.

11. 3. TAMA
Table shows the values of the model parameters obtained at each datum angle using the training data set.

12. 3. TAMA
Estimation Time
To estimate the posture of a user during mobile computing, the accelerometer was attached to a PDA, and the TAMA was implemented on it.

13. 3. TAMA Performance Evaluation
Table V shows the tilt angles measured by the TAMA with 1-s estimation time and 180◦ datum angle.

14. 3. TAMA
These results were compared with the previous research [7] in the range of 0◦ to 70◦ using evaluation factors.

15. 4. User Posture Monitoring
System Architecture

16. 4. User Posture Monitoring
Data Collection Method
The training data sets were collected in our scenario from five subjects that were asked to perform a test: after the initial state of about 5 s, the subjects watched the movie played out by the PDA for about 15 s.

17. 4. User Posture Monitoring
Motion Acceleration Component Elimination
The frequency response curves have their peak values at a specific frequency component when the pole values were complex numbers.

18. 4. User Posture Monitoring
If the pole values were real numbers and the poles were moved to the left half-plane in the z-plane.

19. 4. User Posture Monitoring
when poles were moved to the right half-plane, the skirt characteristic of the SLPF was better, and the SLPF allowed passing the very small low-frequency component.

20. 4. User Posture Monitoring
An experiment was conducted to eliminate the motion acceleration component according to moving of the pole values of the SLPF.
(a) Original time-series acceleration data.
(b)–(e) Time-series acceleration data after filtering: (b) p1 = −0.5 − j0.5, p2 = −0.5 + j0.5; (c) p1 = p2 = −0.6; (d) p1 = p2 = 0.7; (e) p1 = p2 = 0.97.

21. 4. User Posture Monitoring
To find out the proper pole values of the SLPF, the pole values were investigated in the range of 0.95 to 0.99.

22. 4. User Posture Monitoring
Posture Recognition in Three Activities
To determine the range of θ for the posture of a user, a series of threshold analysis tests were run.
The θ in each activity was calculated by the TAMA with the training data set.

23. 4. User Posture Monitoring
The threshold analyses were performed on the training data sets to estimate the posture of a user in each activity, and we examined the values of the optimal threshold to determine the convergence of the posture.

24. 5. Recognition results Two evaluation factors were used as follows:
the ratio of the number of “Display ON” to the number of trials.
the ratio of the number of “Display ON” to the number of malfunctions (“Display OFF”).

25. 5. Recognition results
The recognition accuracy of the UPMS.

26. 6. Conclusion
The TAMA can be used to estimate not only the posture of users with a mobile device, as mentioned in this paper, but also the posture of scoliosis patients and the bent spine posture of musicians, athletes, or public people.
The proposed UI using context-aware computing can automatically recognize the posture of a mobile device user with good accuracy.

27. Thank You !