Abstract
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 a quaternion-based unscented Kalman filter algorithm to fuse inertial/magnetic sensors measurements for tracking human arm movements. In order to have a better estimation of the orientation of the forearm and the upper arm, a constraint equation was developed based on the relative velocity of the elbow joint with respect to the inertial sensors attached to the forearm and the upper arm. Also to compensate for fast body motions, we adapted the measurement covariance matrix in such a way that the filter implements gyroscopes when large accelerations are involved. The proposed algorithm was evaluated experimentally by an optical tracking system as the ground truth reference. The results showed the effectiveness and good performance of the proposed algorithm.
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Bachmann, E.R., McGhee, R.B., Yun, X., Zyda, M.J.: Inertial and magnetic posture tracking for inserting humans into networked virtual environments. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 9–16. ACM (2001)
Chardonnens, J., Favre, J., Cuendet, F., Gremion, G., Aminian, K.: Characterization of lower-limbs inter-segment coordination during the take-off extension in ski jumping. Hum. Mov. Sci. 32(4), 741–752 (2013)
Koenemann, J., Bennewitz, M.: Whole-body imitation of human motions with a Nao humanoid. In: 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 425–425. IEEE (2012)
Seel, T., Raisch, J., Schauer, T.: Imu-based joint angle measurement for gait analysis. Sensors 14(4), 6891–6909 (2014)
Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Signal Process. Control 3(1), 1–18 (2008)
Fern’ndez-Baena, A., Susın, A., Lligadas, X.: Biomechanical validation of upper-body and lower-body joint movements of kinect motion capture data for rehabilitation treatments. In: 2012 4th International Conference on Intelligent Networking and Collaborative Systems (INCos), pp. 656–661. IEEE (2012)
Wei, X., Zhang, P., Chai, J.: Accurate realtime full-body motion capture using a single depth camera. ACM Transactions on Graphics (TOG) 31(6), 188 (2012)
Pfister, A., West, A.M., Bronner, S., Noah, J.A.: Comparative abilities of microsoft kinect and vicon 3d motion capture for gait analysis. J. Med. Eng. Technol. 38(5), 274–280 (2014)
Asteriadis, S., Chatzitofis, A., Zarpalas, D., Alexiadis, D.S., Daras, P.: Estimating human motion from multiple kinect sensors. In: Proceedings of the 6th International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications, p. 3. ACM (2013)
Tao, G., Sun, S., Huang, S., Huang, Z., Wu, J.: Human modeling and real-time motion reconstruction for micro-sensor motion capture. In: 2011 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), pp. 1–5. IEEE (2011)
Shuster, M.D., Oh, S.D.: Three-axis attitude determination from vector observations. J. Guid. Control Dynam. 4(1), 70–77 (1981)
Yun, X., Aparicio, C., Bachmann, E.R., McGhee, R.B.: Implementation and experimental results of a quaternion-based Kalman filter for human body motion tracking. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005, pp. 317–322. IEEE (2005)
Yun, X., Bachmann, E.R., McGhee, R.B.: A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements. IEEE Trans. Instrum. Meas. 57(3), 638–650 (2008)
Fourati, H., Manamanni, N., Afilal, L., Handrich, Y.: Complementary observer for body segments motion capturing by inertial and magnetic sensors. IEEE/ASME Trans. Mechatron. 19(1), 149–157 (2014)
Sabatini, A.M.: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans. Biomed. Eng. 53(7), 1346–1356 (2006)
Sun, S., Meng, X., Ji, L., Huang, Z., Wu, J.: Adaptive Kalman filter for orientation estimation in micro-sensor motion capture. In: 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2011)
Zhang, Z., Huang, Z., Wu, J.: Hierarchical Information Fusion for Human Upper Limb Motion Capture. In: 12th International Conference On Information Fusion, 2009. FUSION’09, pp. 1704–1711. IEEE (2009)
Cooke, J.M.: NPSNET: Flight Simulation dynamic modeling using quaternions. PhD thesis, Monterey, California Naval Postgraduate School (1992)
Yun, X., Lizarraga, M., Bachmann, E.R., McGhee, R.B.: An improved quaternion-based Kalman filter for real-time tracking of rigid body orientation. In: 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings, Vol. 2, pp. 1074–1079. IEEE (2003)
Simon, D.: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley, New York (2006)
Julier, S.J., Uhlmann, J.K.: New extension of the Kalman filter to nonlinear systems. In: Aerosense’97, pp. 182–193. International Society for Optics and Photonics (1997)
Tian, Y., Meng, X., Tao, D., Liu, D., Feng, C.: Upper limb motion tracking with the integration of imu and kinect. Neurocomputing 159, 207–218 (2015)
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Atrsaei, A., Salarieh, H., Alasty, A. et al. Human Arm Motion Tracking by Inertial/Magnetic Sensors Using Unscented Kalman Filter and Relative Motion Constraint. J Intell Robot Syst 90, 161–170 (2018). https://doi.org/10.1007/s10846-017-0645-z
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DOI: https://doi.org/10.1007/s10846-017-0645-z