1
uDirect: A Novel Approach for
Pervasive Observation of User
Direction with Mobile Phones
Dr. Amir Hoseini-Tabatabaei
Dr....
Outline
Introduction
 Application of pervasive direction estimation
 Pervasive direction estimation techniques
uDirect
...
Application of Pervasive
Direction Estimation
 Direction estimation is an essential part of a verity of
applications:
– D...
Pervasive direction estimation
techniques
Sensing Approach Typical sensors Shortcomings Advantages
Ambient sensors
UWB, Wi...
Mobile phone based
techniques and limitations
* Less accurate and more computational expensive than PCA of accelerometer[3...
 Proposed model : uDirect
[5]
uDirect : Requirements
 Requirements
1- Providing a pervasive observation of the user facing direction on
mobile phones
2...
uDirect: Approach
 Estimating user orientation in a global coordinate:
8
Calibration
• Mobile –Earth
Local Direction Esti...
Calibration
uDirect : algorithm design
 Algorithm: performing estimations in two step
9
 Utilizing the acceleration
patt...
Calibration
uDirect : algorithm design
 Algorithm: performing estimations in two step
10[8]
Utilizing measurements
from ...
1. Calibration
 Orientation: calibrating sensor readings with respect to the
reference coordination (Earth)
– Detecting e...
2. Direction Estimation
 We need an estimation of user coordination.
– We only have the vertical (V) direction form calib...
2.1. Direction Estimation
 Assuming the mobile is in user’s trousers pocket.
1- Modelling accelerometer measurements caus...
2.2.Direction Estimations
)GA(
)t(A
oh
oa
rzA))t(ycos(ryA))t(zsin())t(ysin(rxA)t(zcos())t(ysin(
ryA))t(zcos(rxA))t(zsin(
r...
2.3.Direction Estimations
Femur transverse rotation[5]
Polynomial fitted curve
Transverserotation
deviation(D)
Percentage ...
 Initial evaluation and Results
[5]
H&T
Acceleration(m/s^2)Deviationfrom
North(D)
Time (s)
Results. I
Base line
uDirect (Average per step)
DeviationfromNorth(...
Results. II
Algorithm performance
Performance in Comparison
with GPS approach
Baseline
PCA[1]
uDirect (Average per section...
Limitation and future works
• Similar to conventional PCA based approach the current
model is limited to trousers pocket
...
Conclusion
 Developing and evaluating the uDirect as a direction estimation
techniques for mobile phones.
 The model is ...
Thank you
[18]
s.hoseinitabatabaei @ surrey.ac.uk
References
[1] K .Kunze, P. Lukowicz, K. Partridge, and B. Begole, "Which way am i facing:
inferring horizontal device ori...
References
[5] A.S. Levens, V.T. Inman, and J.A. Blosser, "Transverse rotation of the segments of
the of the lower extrimi...
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Udirect: accurate and reliable estimation of the facing direction of the mobile phone users

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Our uDirect technology can accurately estimate the facing direction of a mobile phone user, independent of device position and orientation and without any user intervention.
Knowing which way a user is facing can provide valuable information for the provision of mobile services and applications. The proposed technology utilises inertial sensors that are readily available in mobile consumer devices (e.g. smart phones). While providing high accuracy, the solution is able to cope with arbitrary wearing positions and orientations of the mobile consumer device, making it suitable for use in every-day life situations. This is achieved by an estimation of the user orientation with respect to the reference frame of both sensing module, and earth coordinate system.

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  • Talk about
  • order to adequately render human to computer interaction
  • This approach is very close to wearable approachSuffer from similar However is not considered intrusive and is not faced with such limitations in data collection time.
  • The accuracy of the
  • What ever we sample form user in different time would be in different positions and orientations
  • Get your attention
  • A walking cycle consist of two phases : stance phase and swing phase.
  • General form
  • Idea was to have horizontalEquationLooked through the empirical measurments of rotation of related body segment
  • options
  • Udirect: accurate and reliable estimation of the facing direction of the mobile phone users

    1. 1. 1 uDirect: A Novel Approach for Pervasive Observation of User Direction with Mobile Phones Dr. Amir Hoseini-Tabatabaei Dr.Alex Gluhak Prof. Rahim Tafazolli Centre for Communication and Systems Research – University of Surrey
    2. 2. Outline Introduction  Application of pervasive direction estimation  Pervasive direction estimation techniques uDirect  Approach  Requirements  Algorithm design Evaluation and Results Limitation and Future work Conclusion 2[1]
    3. 3. Application of Pervasive Direction Estimation  Direction estimation is an essential part of a verity of applications: – Dead reckoning – Human computer interaction(HCI) – Social signal processing – And many others [2]
    4. 4. Pervasive direction estimation techniques Sensing Approach Typical sensors Shortcomings Advantages Ambient sensors UWB, Wi-Fi, Bluetooth, Camera, Dependency to infrastructure. (localized applications) No power and computation limitation. No limitation on time Wearable sensors Camera, Accelerometer, Magnetometer and Gyro , GPS, IR , … Short time data collection (Intrusiveness), limited computation and energy resource. No limitation on location . Mobile phone – based Accelerometer , Magnetometer , Gyro, GSM,GPS limited computation and energy resource and phone context problems. No limitation on location and time [3]
    5. 5. Mobile phone based techniques and limitations * Less accurate and more computational expensive than PCA of accelerometer[3] 5 Techniques Limitations 1. Principal Component Analysis (PCA) based approaches • Limited to trousers pocket. • Requires segments of unidirectional movements with appropriate amount of samples. • Susceptible to outliers (not reliable)Gyroscope measurements[5]* Accelerometer measurements[1-3] 2. Heading direction with absolute positioning form Global Positioning Systems (GPS) • Susceptible to shadowing • Limited positioning accuracy (e.g. 8 meter for mobile devices)  Assumption: People normally walk forward [4]
    6. 6.  Proposed model : uDirect [5]
    7. 7. uDirect : Requirements  Requirements 1- Providing a pervasive observation of the user facing direction on mobile phones 2- Addressing the shortcomings of current approaches. (GPS and PCA base models) 3- Addressing the related phone context problems 7[6]
    8. 8. uDirect: Approach  Estimating user orientation in a global coordinate: 8 Calibration • Mobile –Earth Local Direction Estimation • User-Mobile Global Direction estimation • User-Earth X Y Z N E -G [7]
    9. 9. Calibration uDirect : algorithm design  Algorithm: performing estimations in two step 9  Utilizing the acceleration pattern of the body segment (corresponding to device position) for identifying proper moment s in measured data in which user orientation relative to mobile can be estimated. [8]
    10. 10. Calibration uDirect : algorithm design  Algorithm: performing estimations in two step 10[8] Utilizing measurements from Accelerometer and Magnetometer to estimate the relative orientation of phone and earth coordinates
    11. 11. 1. Calibration  Orientation: calibrating sensor readings with respect to the reference coordination (Earth) – Detecting earth coordinates  Gravity: Accelerometer  North : Magnetometer – Calculating transformation components  Computational efficient form by using mathematics of Hilbert’s space and quaternion 11 Dx Dy Dy -G N E H Inclination Declination [9]
    12. 12. 2. Direction Estimation  We need an estimation of user coordination. – We only have the vertical (V) direction form calibration.  How to find the F and S ?  First assuming the user coordinate is known: I. What mobile phone measures during forward walking . II. Transfer the measurements back to user coordinate . III. Focus on behaviour of horizontal components during walking locomotion. 12[10]
    13. 13. 2.1. Direction Estimation  Assuming the mobile is in user’s trousers pocket. 1- Modelling accelerometer measurements caused by thigh movement during walking locomotion. Acceleration on mobile coordinate yrzAycosxAysin zrzAzsinysinyAcosxAycoszsin zryrzA)t(ysin)t(zcosyA)t(zsinxA)t(ycos)t(zcos 22 oa )t(A    [11] Rotation Quaternion : R(t) Rotational Acceleration: Ar(t) Translational Acceleration: Ah(t) Aoa = Ar(t) + R(t)(Ah(t)+G)R(t)*
    14. 14. 2.2.Direction Estimations )GA( )t(A oh oa rzA))t(ycos(ryA))t(zsin())t(ysin(rxA)t(zcos())t(ysin( ryA))t(zcos(rxA))t(zsin( rzA)t(ysin(ryA))t(zsin())t(ycos(rxA))t(zcos()t(ycos( Acceleration on user coordinate [12] 2- Modelling the side acceleration pattern on user coordinate yhzzz 2 y 2 z )AoG()t(r))t(cos())t(r)t(r))(t(sin(As 
    15. 15. 2.3.Direction Estimations Femur transverse rotation[5] Polynomial fitted curve Transverserotation deviation(D) Percentage of walking cycle Reconstructed Acceleration(m/s^2) z  Swing Phase Stance Phase Heel Strike Toe off [13] 3- Finding the proper moment for estimation Heel Strike and toe of moments can be detected as local and global minima of tight vertical acceleration pattern[6].
    16. 16.  Initial evaluation and Results [5]
    17. 17. H&T Acceleration(m/s^2)Deviationfrom North(D) Time (s) Results. I Base line uDirect (Average per step) DeviationfromNorth(D) Steps[14]
    18. 18. Results. II Algorithm performance Performance in Comparison with GPS approach Baseline PCA[1] uDirect (Average per section) Techniques Mean error (Degree) Section1 Section 2 Section 3 Section 4 Model from [1] 26.7 37.6 10.5 37.0 uDirect(averag ed per section) 18.9 41.7 37.3 35.4 Technique Mean error (Degree) Standard deviation Model from [1] +7.999 +1.253 uDirect(averaged per section) +0.162 -0.603 uDirect (averaged per step) -10.5 -11.8 DeviationfromNorth(D) Steps
    19. 19. Limitation and future works • Similar to conventional PCA based approach the current model is limited to trousers pocket  Extending the approach to other main positions[7] : shoulder bags, chest pocket and belt – enhancement positions • The estimations degrades in shorter sections  To adaptively select the estimation model  To add the magnetic field based-tracking for reducing power consumption. [16]
    20. 20. Conclusion  Developing and evaluating the uDirect as a direction estimation techniques for mobile phones.  The model is based on physiological characteristics of human walking locomotion.  Evaluations of the algorithm with a simple proof of concept implementation confirmed the assumptions of our analytical modeling  Orientation independent approach  Dose not face with GPS approach constrains (shadowing and minimum distance)  Direction estimations in contrast to PCA do not require an additional segmentation.  Independent estimation at each step makes it prone to error accumulation.  uDirect is shown to be more accurate and reliable than conventional GPS and PCA based models for paths longer than 2 steps 20[17]
    21. 21. Thank you [18] s.hoseinitabatabaei @ surrey.ac.uk
    22. 22. References [1] K .Kunze, P. Lukowicz, K. Partridge, and B. Begole, "Which way am i facing: inferring horizontal device orientation from an accelerometer signal," in Wearable Computers, 2009. ISWC '09. International Symposium on, Linz, 2009, pp. 149-150. [2] M. Kourogi and T. Kuratta, "A wearable augmented reality system with personal positioning based on walking locomotion analysis," in Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality, Tokyo, 2003, p. 342. [3] U.STEINHOFF, B.SCHIELE,2010. Dead Reckoning from the Pocket - An Experimental Study. In Eighth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom2010), 2010. [4] U. Blanke and B. Schiele, “Sensing location in the pocket,” in Adjunct Poster Proceedings UbiComp’08, 2008. [19]
    23. 23. References [5] A.S. Levens, V.T. Inman, and J.A. Blosser, "Transverse rotation of the segments of the of the lower extrimity in locomotion," The journal of bone and joint surgery, vol. 30, pp. 859-872, 1948. [6] K Aminian, K. Rezakhanlou, E.D. Andres, and C. Fritsch, "Temporal feature estimation during walking using minitaure accelerometer: an analysis of gait improvement after hip arthoplasty," Journal of Medical & Biological Engineering & Computing, vol. 37, no. 6, pp. 686-691, 1999. [7] F. Ichikawa, J. Chipchase, and R. Grignani, "Where's the phone? A study of mobile phone location in public spaces," in International Conference on Mobile Technology, Applications and Systems, 2005 2nd, Guangzhou, 2005, pp. 1-8. [20]

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