A New User Adaptive Pointing and Correction Algorithm

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 235)


In this paper, we propose a new user-adaptive pointing and correction algorithm applied in the field of smart sensing. The error from the accelerometer sensor’s output must be carefully managed as the sensor is more sensitive to data change compared to that of gyroscope sensor. Thus, we minimize noise by applying the Kalman filtering to data for each axis from the accelerometer. In addition, we can also alleviate hand tremor effectively by applying the Kalman filter to the data variation for x and y. In this study, we obtain a tilt compensation by applying the compensation algorithm on acceleration of the gravity of the extracted data. Moreover, in order to correct the inaccuracy on smart sensors due to the rapid movement of a device, we propose a hybrid genetic approach.


MEMS sensor Pointing and correction Quaternion Kalman filter Tilt compensation Genetic algorithm 



This research was supported by the Ministry of Education, Science Technology (MEST) and National Research Foundation of Korea (NRF) through the Human Resource Training Project for Regional Innovation.(No. 2012-04A0301912010100).


  1. 1.
    Song JY (2011) Smart TV and mobile devices, the paradigm of change and the development of the smart media, KT Institute of economic and business administrationGoogle Scholar
  2. 2.
    Kim A, Golnaraghi MF (2004) Initial calibration of an inertial measurement unit using an optical position tracking system. IEEE position location and navigation symposium, pp 96−101Google Scholar
  3. 3.
    Kim JR, Jeong IB (2011) Multiple dimension user motion detection system based on wireless sensors. KIICE 15(3):700–712Google Scholar
  4. 4.
    Jang DS (2008) Implementation and Effectiveness of the Rotational Transform Using the Quaternion, Inst Ind Technol 31(1):351−357Google Scholar
  5. 5.
    Kuipers JB (2000) Quaternions & rotation sequences. Coral Press, Department of Mathmatics, Calvin College, Grand rapids, MI 49546, USA, Princeton, pp 127−143Google Scholar
  6. 6.
    Ozyagcilar T (2012) Implementing a tilt-compensated eCompass using accelerometer and magnetometer sensors. Freescale semiconductor, AN 4248, Rev. 3Google Scholar
  7. 7.
    Welch G, Bishop G (2006) An Introduction to the Kalman Filter, UNC-Chapel Hill, Technical report, pp 95−041Google Scholar
  8. 8.
    Sohn BK, Lee KM (2004) A coordinated collaboration method of multiagent systems based on genetic algorithms. KIIS 14(2):156–163Google Scholar
  9. 9.
    Yalcinoz T, Altun H, Uzam M (2001) Economic dispatch solution using a genetic algorithm based on arithmetic crossover. In: IEEE porto power tech conference 10−13 Sept 2001Google Scholar
  10. 10.
    Smith J, Fogarty TC (1996) Self adaptation of mutation rates in a steady state genetic algorithm. In: IEEE international conference, pp 318−323Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Electronic and Computer EngineeringChonnam National UniversityGwangjuKorea

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