南台科技大學 資訊工程系 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 Outline Introduction 1 Sensors 2 TAMA 3 User Posture Monitoring 4 Recognition results 5 Conclusion 6

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 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 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.

南台科技大學 資訊工程系