Eric R. Bachmann
ABSTRACT
Rigid body orientation can be determined without the aid of a generated source using nine-axis MARG (Magnetic field, Angular Rate, and Gravity) sensor unit containing three orthogonally mounted angular rate sensors, three orthogonal linear accelerometers and three orthogonal magnetometers. This paper describes a quaternion-based complementary filter algorithm for processing the output data from such a sensor. The filter forms the basis for a system designed to determine the posture of an articulated body in real-time. In the system the orientation relative to an Earth-fixed reference frame of each limb segment is individually determined through the use of an attached MARG sensor. The orientations are used to set the posture of an articulated body model. Details of the fabrication of a prototype MARG sensor are presented. Calibration algorithms for the sensors and the human body model are also presented. Experimental results demonstrate the effectiveness of the tracking system and verify the correctness of the underlying theory.
- 1.Bachmann, E., McGhee, R., Yun X. & Zyda, M., "Real-Time Tracking and Display of Human Limb Segment Motions Using Sourceless Sensors and a Quaternion-Based Filtering Algorithm - Part II: Calibration and Implementation," MOVES Academic Group Technical Report NPS-MV-01- 003, Naval Postgraduate School, Monterey, CA., 2001 (Available at http://npsnet.org/moves/TechReports/NPS- MV-01-003.pdf).Google Scholar
- 2.Bachmann, E., Inertial and Magnetic Angle Tracking of Limb Segments for Inserting Humans into Synthetic Environments, Ph.D. dissertation, Naval Postgraduate School, Monterey, CA., 2000. (Available at http://npsnet.org/bachmann/)Google Scholar
- 3.Bachmann, E., Duman I., Usta, U., McGhee R., Yun, X., & Zyda, M., "Orientation tracking for Humans and Robots Using Inertial Sensors," International Symposium on Computational Intelligence in Robotics & Automation (CIRA 99), Monterey, CA, pp.187-194, 1999.Google Scholar
- 4.Bray, J. & Sturman, C., Bluetooth: Connect without Cables, Prentice Hall, Inc., 2000. Google ScholarDigital Library
- 5.Brown, R. & Hwang, P., Introduction to Random Signals and Applied Kalman Filtering, Second Edition, John Wiley and Sons, Inc., New York, NY., 1992.Google Scholar
- 6.Caruso, M., Set/Reset Pulse Circuits for Magnetic Sensors, Honeywell, Inc., AN-201., 1995.Google Scholar
- 7.Cormen, T., Leiserson, C., & Rivest, R. Introduction to Algorithms, McGraw-Hill, New York, 1994. Google ScholarDigital Library
- 8.Crossbow Inc., Crossbow CXL04M3 Data Sheet., 1998.Google Scholar
- 9.Durlach, N. & Mayor, A. (Eds.), National Research Council, Virtual Reality: Scientific and Technological Challenges, National Academy Press, Washington, D.C., pp. 188-204, 306-317, 1995.Google Scholar
- 10.Funda, J., Taylor, R., & Paul, R., "On Homogeneous Transforms, Quaternions, and Computational Efficiency," IEEE Transactions on Robotics and Automation, Vol. 6, No. 3, pp. 382-388, 1990.Google ScholarCross Ref
- 11.Honeywell Inc., Honeywell Three-Axis Magnetic Sensor Hybrid, HMC2003., 1998.Google Scholar
- 12.IntegriNautics Corporation, Private communication with development engineer. Menlo Park, CA 94025, 2000.Google Scholar
- 13.Kuipers, J., Quaternions and Rotation Sequences, Princeton University Press, Inc., Princeton, NJ, 1998.Google Scholar
- 14.Joao Luis Marins, Xiaoping Yun, Eric R. Bachmann, Robert B. McGhee, and Michael J. Zyda, "An Extended Kalman Filter for Quaternion-Based Orientation Estimation Using MARG Sensors," Proc. of 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 29-November 3, 2001, Maui, Hawaii, USA.Google Scholar
- 15.McGhee, R., Bachmann, E., Yun X. & Zyda, M. "Real-Time Tracking and Display of Human Limb Segment Motions Using Sourceless Sensors and a Quaternion-Based Filtering Algorithm - Part I: Theory," MOVES Academic Group Technical Report NPS-MV-01-001, Naval Postgraduate School, Monterey, CA. 2000. (Available at http://npsnet.org/moves/TechReports/NPS- MV-01-001.pdf)Google Scholar
- 16.McGhee, R., Nakano, E., Koyachi, N., & Adachi, H., "An Approach to Computer Coordination of Motion for Energy- Efficient Walking Machines," Bulletin of Mechanical Engineering Laboratory, Japan, No. 43., 1986.Google Scholar
- 17.McGhee, R., "Some Parameter-Optimization Techniques," Digital Computer User's Handbook, McGraw-Hill, pp. 234- 253, 1967.Google Scholar
- 18.McKinney, D., McKinney Technology, 9 Glen Avenue, Prunedale, CA, 93907, [email protected] .Google Scholar
- 19.Meyer, K., Applewhite, H., & Biocca, F., "A Survey of Position Trackers," Presence: Teleoperators and Virtual Environments, Vol. 1, No. 2, pp. 173-200, 1992. Google ScholarDigital Library
- 20.Molet, T., Aubel, A., Tolga, C., Carion, S., Lee, E., Naagnenat-Thalmann, N., Hoser, H., Pandzic, I., Sannier, G., & Thalmann, D., "Anyone for Tennis," Presence: Teleoperators and Virtual Environments, Vol. 8, No. 2, MIT Press, Cambridge MA., pp. 140-156, 1999. Google ScholarDigital Library
- 21.Nixon, M., McCallum, B., Fright, W., & Price, N. "The effects of Metals and Interfering Fields on Electromagnetic Trackers," Presence: Teleoperators and Virtual Environments, Vol. 7, No. 2, MIT Press, Cambridge, MA, pp. 204-218, 1998. Google ScholarDigital Library
- 22.Tokin American Inc., Tokin CG16D Solid State Rate Sensor Data Sheet, http:// www.tokin.com/ Tokin_America_Products/46/p46c/p46c.html, 1998.Google Scholar
Recommendations
Human Arm Motion Tracking by Inertial/Magnetic Sensors Using Unscented Kalman Filter and Relative Motion Constraint
Human motion tracking has many applications in biomedical and industrial services. Low-cost inertial/magnetic sensors are widely used in human motion capture systems to obtain the orientation of the human body segments. In this paper, we have presented ...
Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion
Highlights- EKF approach is applied to reduce uncertainty and produce better estimations for PDR.
AbstractThe challenges of self-contained sensor based pedestrian dead reckoning (PDR) are mainly sensor installation errors and path integral errors caused by sensor variance, and both may dramatically decrease the accuracy of PDR. To address ...
Hybrid Inertial-Robotic Motion Tracking for Posture Biofeedback in Upper Limb Rehabilitation*
2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)Rehabilitation robotics and neuromuscular stimulation have become widespread technologies for rehabilitation training of stroke and spinal cord injured patients. In this context, real-time tracking of the performed motion facilitates real-time control of ...
Comments