DOI: 10.17586/2226-1494-2017-17-5-798-804


TRACKING AND CLASSIFICATION OF HEAD MOVEMENT BY INERTIAL MEASUREMENT UNIT DATA

A. I. Shchekoldin, N. Y. Dema, A. D. Shevyakov , S. A. Kolyubin


Read the full article 
Article in Russian

For citation: Shchekoldin A.I., Dema N.Yu., Shevyakov A.D., Kolyubin S.A. Tracking and classification of head movement by inertial measurement unit data. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 5, pp. 798–804 (in Russian). doi: 10.17586/2226-1494-2017-17-5-798-804

Abstract

Subject of Research.  We present the algorithm for tracking and classification of the operator's head movement according to the data obtained from an IMU (inertial measuring unit) installed in the helmet of virtual reality. We performed experimental testing of the developed algorithm and proposed the way of its application for control of mobile robot with six degrees of freedom. Method.  The problem is solved by complex filtering of input signals from the gyroscope with the Kalman filter and the dead-zone filter. The definition of the operator's head movement pattern is based on determination coefficient  calculation and its comparison with the threshold level. Main Results. We have developed an algorithm for unambiguous evaluation of the operator's head movement that can be interpreted by the control system as a control signal. Practical Relevance. Presented algorithms can be used in a wide range of systems, for example, for controlling mobile telepresence robots that have special suspension for camera guidance control and are controlled with the use of virtual reality helmet. The developed algorithm will be applied also in the implementation of mobile robotic platform, equipped with computer vision systems, navigation and augmented reality, that is planned to be created in the framework of practice-oriented research and development work at ITMO University.


Keywords: motion detection, signal filtering, Kalman filter, death-zone filter, telepresence robot

References
 1.     Kade D., Aksit K., Urey H., Ozcan O. Head-mounted mixed reality projection display for games production and entertainment. Personal and Ubiquitous Computing, 2015, vol. 19, no. 3-4, pp. 509–521. doi: 10.1007/s00779-015-0847-y
2.     Chow J., Feng H., Amor R., Wunsche B.C. Music education using augmented reality with a head mounted display. Proc. 4th Australasian User Interface Conference, AUIC2013. Adelaide, Australia, 2013, vol. 139, pp. 73–79.
3.     Caudell T.P., Mizell D.W. Augmented reality: an application of heads-up display technology to manual manufacturing processes. Proc. 25th Hawaii Int. Conf. on System Sciences. Kauai, USA, 1992, vol. 2, pp. 659–669. doi: 10.1109/hicss.1992.183317
4.     Livingston M.A. et al. An augmented reality system for military operations in urban terrain. Proc. Interservice/Industry Training, Simulation, and Education Conference, I/ITSEC’02. Orlando, Florida, 2002, pp. 1–8.
5.     Kot T., Novak P. Utilization of the Oculus Rift HMD in mobile robot teleoperation. Applied Mechanics and Materials, 2014, vol. 555, pp. 199–208. doi: 10.4028/www.scientific.net/amm.555.199
6.     Choi W.H., Kim D.U., Jie M.S. Development of 3-dimensional motion recognition based wireless transceiver for HMD. Advanced Science and Technology Letters, 2016, vol. 140, pp. 129–133. doi: 10.14257/astl.2016.140.25
7.     Grabovskaya E.Yu., Pavlova S.V. Analysis of strategies of proving information to pilot/airplane interface. Kibernetika i Vychislitel'naya Tekhnika, 2013, no. 1, pp.78–88.(In Russian)
8.     Karpov A.A., Ronzhin A.L., Usov V.M. Instrumental methods of testing contactless human-machine interaction when using a helmet-mounted display. Pilotiruemye Polety v Kosmos, 2015, no. 3, pp. 43–53.(In Russian)
9.     Kucheryavyi A.A. On-Board Information Systems. Lecture Course. Eds. V.A. Mishin, G.I. Klyuev. 2nd ed. Ul'yanovsk, UlSTU Publ., 2004, 504 p. (In Russian)
10.  Sebryakov G.G., Burlak E.A., Nabatchikov A.M. Adaptive algorithms for software control of sighting devices of remotely-piloted aircrafts. Proc. XII All-Russia Meeting on Control Problems. Moscow, 2014, pp. 6408–6413. (In Russian)
11.  Priandani N.D., Tolle H., Utaminingrum F. Real time advanced head movement recognition for application controller based on Android internal gyroscope sensor. International Journal of Advances in Soft Computing and its Applications, 2017, vol. 9, no. 1, pp. 70–87.
12.  Raisy C.D., Vashisth S., Salhan A.K. Real time acquisition of EMG signal and head movement recognition. International Journal of Computer Applications, 2013, vol. 73, no. 1, pp. 19–22. doi: 10.5120/12705-9501
13.  Lee D., Lim H. Virtual reality contents using the OculusLift and Kinect. Proceedings of the MCSI, 2015, vol. 8, pp. 102–105.
14.  Curtis D., Mizell D., Gruenbaum P., Janin A. Several devils in the details: making an AR application work in the airplane factory. Proc. IEEE Workshop on Augmented Reality, IWAR’98. San Francisco, USA, 1998, pp. 47–60.
15.  Stone R. Virtual reality and telepresence. Robotica, 1992, vol. 10, no. 5, pp. 461–467. doi: 10.1017/S0263574700010663
16.  Angeli D. Almost global stabilization of the inverted pendulum via continuous state feedback. Automatica, 2001, vol. 37, no. 7, pp. 1103–1108.doi: 10.1016/s0005-1098(01)00064-4
17.  Zampieri G. Nonholonomic versus vakonomic dynamics. Journal of Differential Equations, 2000, vol. 163, no. 2, pp. 335–347. doi: 10.1006/jdeq.1999.3727
18.  Yoon M.G. Dynamics and stabilization of a spherical inverted pendulum on a wheeled cart. International Journal of Control, Automation and Systems, 2010, vol. 8, no. 6, pp. 1271–1279. doi: 10.1007/s12555-010-0612-y
19.  LoPresti E., Brienza D.M., Angelo J. Computer head control software to compensate for neck movement limitations. Proc. 2000 Conf. on Universal Usability, UCC. Arlington, USA, 2000, pp. 147–148. doi: 10.1145/355460.355551
20.  Zhang P., Gu J., Milios E.E., Huynh P. Navigation with IMU/GPS/digital compass with unscented Kalman filter. IEEE Int. Conf. on Mechatronics and Automation. Niagara Falls, Canada, 2005, vol. 3, pp. 1497–1502. doi: 10.1109/icma.2005.1626777
21.  Sukkarieh S., Nebot E.M., Durrant-Whyte H.F. A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Transactions on Robotics and Automation, 1999, vol. 15, no. 3, pp. 572–578. doi: 10.1109/70.768189
22.  Julier S.J., Uhlmann J.K. New extension of the Kalman filter to nonlinear systems. Proc. SPIE, 1997, vol. 3068. doi: 10.1117/12.280797
23.  Xie L., Soh Y.C., de Souza C.E. Robust Kalman filtering for uncertain discrete-time systems. IEEE Transactions on Automatic Control, 1994, vol. 39, no. 6, pp. 1310–1314. doi: 10.1109/9.293203
24.  Nagelkerke N.J.D. A note on a general definition of the coefficient of determination. Biometrika, 1991, vol. 78, no. 3, pp. 691–692. doi: 10.2307/2337038
25.  Dougherty E.R., Kim S., Chen Y. Coefficient of determination in nonlinear signal processing. Signal Processing, 2000, vol. 80, no. 10, pp. 2219–2235. doi: 10.1016/s0165-1684(00)00079-7
26.  Hilfert T., Konig M. Low-cost virtual reality environment for engineering and construction. Visualization in Engineering, 2016, vol. 4, no. 1, pp. 2. doi: 10.1186/s40327-015-0031-5
Lavalle S.M., Yershova A., Katsev M., Antonov M. Head tracking for the Oculus Rift. Proc. IEEE Int. Conf. on Robotics and Automation, 2014, pp. 187–194. doi: 10.1109/icra.2014.6906608


Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright 2001-2018 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.

Яндекс.Метрика