|
Vol: 61(75) No: 1 / March 2016 A New Sensor Array System and Methods for the Measurement of 3D Body and Parts Movements Shahidul Islam Mgestyk Technologies, Inc., 80 Aberdeen Street, Suite 220, K1S 5R5, Ottawa, Ontario, Canada, phone: (613) 591-1210 x 203, e-mail: shahid@mgestyk.com, web: http://www.mgestyk.com Cristian Gadea NCCT Laboratory, University of Ottawa, School of Electrical Engineering and Computer Science, 161 Louis Pasteur, Room B-306, K1N 6N5, Ottawa, Canada Bogdan Ionesu Mgestyk Technologies, Inc., 80 Aberdeen Street, Suite 220, K1S 5R5, Ottawa, Ontario, Canada, e-mail: bogdan@mgestyk.com, web: http://www.mgestyk.com Dan Ionescu NCCT Laboratory, University of Ottawa, School of Electrical Engineering and Computer Science, 161 Louis Pasteur, Room B-306, K1N 6N5, Ottawa, Canada Keywords: inertial measurement unit, sensor array, magnetic angular rate, pose estimation, virtual environments. Abstract The recently renewed consumer interest in virtual reality (VR) has created significant demand for accurate full-body tracking solutions. Existing full-body motion tracking solutions from academia and industry have so far required elaborate wired installations, exotic sensor components, lengthy calibration procedures, and were subject to accumulated error. This paper describes a Sensor Array System that uses multiple MEMS-based Inertial Measurement Units (IMUs) for calculating the complete body poses of a subject in a dynamic environment. The modular design and wireless connectivity of the sensor system allows the user to get into and out of the gear quickly and move around freely without having to worry about wires. The system can generate full-body orientation frames at a rate higher than 100Hz. This system can be used to augment the human-computer interface devices used to control already-existing VR systems by mapping the one-to-one movements of the user into the virtual world and producing accurate data in real-time, thereby enhancing the 3D immersive experience of the user. The paper describes the motivations for the architecture selected. The detailed design and implementation of a functional full-body sensor system is presented, including the pose estimation calculations required for tracking the moving body parts. The observed accuracy of the system is also discussed. References [1] R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohli, J. Shotton, S. Hodges and A. Fitzgibbon, “KinectFusion: Real-Time Dense Surface Mapping and Tracking”. Microsoft Researh. Publications; [Online]. Available: http://research.microsoft.com/pubs/155378/ismar2011.pdf (2011). [2] V. Frati, and D. Prattichizzo, “Using Kinect for hand tracking and rendering in wearable haptics”, in IEEE World Haptics Conf. 2011. [3] D. Bassily, C. Georgoulas, J. Güttler, T. Linner, T. Bock and TU München, “Intuitive and Adaptive Robotic Arm Manipulation using the Leap Motion Controller”, Conf. ISR ROBOTIK, Germany 2014. [4] MPU-9250™ MotionTracking device datasheet. Invensense Inc. [Accessed: February 2015]. [Online]. Available: http://www.invensense.com/mems/gyro/documents/PS-MPU-9250A-01.pdf (2015). [5] R. Mahony, T. Hamel and J.-M. Pflimlin, “Nonlinear Complementary Filters on the Special Orthogonal Group,” in IEEE Trans. on Automatic Control, vol. 53, no. 5, 2008, pp. 1203–1218. [6] S.O.H. Madgwick, A.J.L. Harrison and R. Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” in IEEE Int. Conf. on Rehabilitation Robotics, 2011. [7] H. Rehbinder and B.K. Ghosh, “Pose estimation using line-based dynamic vision and inertial sensors,” in Automatic Control, IEEE Transactions on, vol.48, no.2, Feb. 2003, pp.186–199. [8] S.I. Roumeliotis, A.E. Johnson and J.F. Montgomery, “Augmenting inertial navigation with image-based motion estimation,” in Robotics and Automation, IEEE Int. Conf., vol.4, 2002, pp.4326–4333. [9] L. Pei-Chun, H. Komsuoglu and D.E. Koditschek, “Sensor data fusion for body state estimation in a hexapod robot with dynamical gaits,” IEEE Trans. Robotics, vol. 22, no. 5, pp. 932–943, 2006. [10] D. Roetenberg, H. Luinge and P. Slycke. “MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors.” Technical Report, Xsens Technologies, 2008. [11] H.J. Luinge and P. H. Veltink, “Inclination measurement of human movement using a 3-d accelerometer with autocalibration,” in IEEE Trans. Neural Syst. and Rehab. Eng. Vol. 12, pp. 112–121, Mar. 2004. [12] H. Zhou and H. Hu, “Human motion tracking for rehabilitation–a survey,” Biomedical Signal Processing and Control, vol. 3, no. 1, pp. 1–18, 2008. [13] D. Ionescu, B. Ionescu, C. Gadea, and S. Islam, “Multimodal Control of Virtual Game Environments through Gestures and Physical Controllers,” in Conf. on Virtual Environments, Human-Computer Interfaces and Measurement Systems. IEEE, Sep. 2011. |