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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
南台科技大學
資訊工程系
Outline
2
1
Introduction
2
Sensors
3
TAMA
4
User Posture Monitoring
5
Recognition results
6
Conclusion
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.
3
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).
4
2. Sensors
Typical output values of the accelerometer due to gravity.
5
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.
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3. TAMA
 Signal Processing for Measuring the Tilt Angle
7
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.
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3. TAMA
 Compensation and Reference Vectors
 We define the offset errors and the reference vectors as the
model parameters of the TAMA.
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3. TAMA
 The equations for the model parameters and compensation
for each axis in each datum angle.
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3. TAMA
 Table shows the values of the model parameters obtained at
each datum angle using the training data set.
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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.
12
3. TAMA
 Performance Evaluation
 Table shows the tilt angles measured by the TAMA with 1-s
estimation time and 180◦ datum angle.
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3. TAMA
 These results were compared with the previous research [7]
in the range of 0◦ to 70◦ using evaluation factors.
14
4. User Posture Monitoring
 System Architecture
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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”).
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5. Recognition results
 The recognition accuracy of the UPMS.
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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.
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南台科技大學
資訊工程系