Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first last
cannonical link button


Bulletin of the Polish Academy of Sciences. Technical Sciences

Tytuł artykułu

The dynamics of the human arm with an observer for the capture of body motion parameters

Autorzy Babiarz, A.  Bieda, R.  Jaskot, K.  Klamka, J. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN The paper presents an analysis of a mathematical model of the human arm dynamics in terms of observability. The purpose of the performed experiments is the selection of an observer for the possibility of arm tracking. The arm model is based on the two-link manipulator moving horizontally and vertically. For the study a model was linearized and the model part responsible for the work of human muscles was omitted. The experimental part involved simulated measurements of the motion parameters that imitate real-IMU (Inertial Measurement Unit) measurements. Finally, the simulation results using the observer in the form of a Kalman filter and the particle filter have been presented.
Słowa kluczowe
EN human arm   observability   Kalman filter   particle filter  
Wydawca Polska Akademia Nauk, Wydział IV Nauk Technicznych
Czasopismo Bulletin of the Polish Academy of Sciences. Technical Sciences
Rocznik 2013
Tom Vol. 61, nr 4
Strony 955--971
Opis fizyczny Bibliogr. 46 poz., rys., wykr., tab.
autor Babiarz, A.
  • Institute of Automatic Control, Silesian University of Technology, 16 Akademicka St., 44-101 Gliwice, Poland,
autor Bieda, R.
  • Institute of Automatic Control, Silesian University of Technology, 16 Akademicka St., 44-101 Gliwice, Poland
autor Jaskot, K.
  • Institute of Automatic Control, Silesian University of Technology, 16 Akademicka St., 44-101 Gliwice, Poland
autor Klamka, J.
  • Institute of Automatic Control, Silesian University of Technology, 16 Akademicka St., 44-101 Gliwice, Poland
[1] M. Lazarević, “Mechanics of human locomotor system”, FME Transactions 34 (2), 105-114 (2006).
[2] D. Lee, M. Glueck, A. Khan, E. Fiume, and K. Jackson, “A survey of modeling and simulation of skeletal muscle”, ACM Trans. on Graphics 28 (4), (2010).
[3] J.K. Lee and E.J. Park, “A fast quaternion-based orientation optimizer via virtual rotation for human motion tracking”, IEEE Trans. on Biomedical Engineering 56 (5), 1574-1582 (2009).
[4] H. van der Kooij, B. Koopman, and F.C.T. van der Helm, “Human motion control”, Reader for Delft University course wb2407 and Twente University course 115047, 211-220 (2008).
[5] Z.Q. Zhang, W.C. Wong, and J.K. Wu, “Ubiquitous human upper-limb motion estimation using Wearable sensors”, IEEE Trans. on Information Technology in Biomedicine 15 (4), 513-521 (2011).
[6] E. Burdet, K.P. Tee, I. Mareels, T.E. Milner, C.M. Chew, D.W. Franklin, R. Osu, and M. Kawato, “Stability and motor adaptation in human arm movements”, Biological Cybernetics 94 (1), 20-32 (2006).
[7] A. Kadiallah, D.W. Franklin, and E. Burdet, “Generalization in adaptation to stable and unstable dynamics”, PLoS ONE 7 (10), CD-ROM (2012).
[8] M.J. Fu and M.C. Cavusoglu, “Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface”, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 42 (6), 1633-1644 (2012).
[9] W. Li, “Optimal control for biological movement systems”, Ph.D. Thesis, University of California, San Diego, 2006.
[10] D. Liu and E. Todorov, “Hierarchical optimal control of a 7- DOF arm model”, IEEE Symp. on Adaptive Dynamic Programming and Reinforcement Learning 1, 50-57 (2009).
[11] P.R. Culmer, A.E. Jackson, S. Makower, R. Richardson, J.A.Cozens, M.C. Levesley, and B.B. Bhakta, “A control strategy for upper limb robotic rehabilitation with a dual robot system”, IEEE/ASME Tran. on Mechatronics 15 (4), 575-585 (2010).
[12] P.K. Artemiadis and K.J. Kyriakopoulos, “EMG-based control of a robot arm using low-dimensional embeddings”, IEEE Trans. on Robotics 26 (2), 393-398 (2010).
[13] P.K. Artemiadis and K.J. Kyriakopoulos, “A switching regime model for the EMG-based control of a robot arm”, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 41 (1), 53-63 (2011).
[14] F.I. Sheikh, “Real-time human arm motion translation for the WorkPartner robot”, Master Thesis, Lule˚a University of Technology, Kiruna, 2008.
[15] G. Angelosanto, “Kalman filtering of IMU sensor for robot balance control”, Bachelor Thesis, Massachusetts Institute of Technology, Cambridge, 2008.
[16] M. El-Gohary and J. McNames, “Shoulder and elbow joint angle tracking with inertial sensors”, IEEE Trans. on Biomedical Engineering 59 (9), 2635-2641 (2012).
[17] H. Fourati, N. Manamanni, L. Afilal, and Y. Handrich, “Complementary observer for body segments motion capturing by inertial and magnetic sensors”, IEEE/ASME Trans. on Mechatronics 1, 1-9 (2012).
[18] S.H. Lee, “Biomechanical modeling and control of the human body for computer animation”, Ph.D. Thesis, University of California, Los Angeles, 2008.
[19] Y.S. Suh, “Orientation estimation using a quaternion-based indirect Kalman filter with adaptive estimation of external acceleration”, IEEE Trans. on Instrumentation and Measurement 59 (12), 3296-3305 (2010).
[20] A. Rodriguez-Angeles, A. Morales-Diaz, J.C. Bernabeˇe, and G. Arechavaleta, “An online inertial sensor-guided motion control for tracking human arm movements by robots”, Proc. 3rd IEEE RAS and EMBS Int. Conf. on Biomedical Robotics and Biomechatronics (BioRob) 1, 319-324 (2010).
[21] A. Wang and M. Deng, “Human arm-like robot control using the viscoelasticity of human multi-joint arm”, Proc. 5th Int. Conf. on Computer Sciences and Convergence Information Technology (ICCIT) 1, 738-743 (2010). 22] J. Yu, J. Zhong-Ping, and Q. Ning, “Optimal control mechanisms in human arm reaching movements”, Proc. 30th Chinese Control Conference (CCC) 1, 1377-1382 (2011).
[23] P.H. Chang, K. Park, S. Hoon Kang, H.I. Krebs, and N.Hogan, “Stochastic estimation of human arm impedance using robots with nonlinear frictions: an experimental validation”, IEEE/ASME Transactions on Mechatronics 18 (2), 775-786 (2013).
[24] H. Moon, N. Hoang, N.P. Robson, and R. Langari, “Human arm motion planning against a joint constraint”, Proc. 4th IEEE RAS & EMBS Int. Conf. on Biomedical Robotics and Biomechatronics (BioRob) 1, 401-406 (2012).
[25] N. Klopˇcar and J. Lenarˇciˇc, “Kinematic model for determination of human arm reachable workspace”, Meccanica 40 (2), 203-219 (2005).
[26] W. Gallagher, M. Ding, and J. Ueda, “Relaxed individual control of skeletal muscle forces via physical human-robot interaction”, Multibody System Dynamics 30 (1), 77-99 (2013).
[27] P.K. Artemiadis, P.T. Katsiaris, and K.J. Kyriakopoulos, “A biomimetic approach to inverse kinematics for a redundant robot arm”, Autonomous Robots 29 (3-4), 293-308 (2010).
[28] R.R. Porle, A. Chekima, F. Wong, and G. Sainarayanan, “Multiple features integration in 2D upper human body pose modelling system”, Proc. 10th Int. Conf. on Information Sciences Signal Processing and their Applications (ISSPA) 1, 570-573 (2010).
[29] Y.R. Chen, C.M. Huang, and L.C. Fu, “Visual tracking of human head and arms with a single camera”, Proc. IEEE/RSJ Int.Conf. on Intelligent Robots and Systems (IROS) 1, 3416-3421 (2010).
[30] T. Kashima, K. Yanagihara, and M. Iwaseya, “Trajectory formation based on a human arm model with redundancy”, Proc.IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC) 1, 959-963 (2012).
[31] S. Djebrani, A. Benali, and F. Abdessemed, “Modelling and control of an omnidirectional mobile manipulator”, Int. J. Applied Mathematics and Computer Science 22 (3), 601-616 (2012).
[32] T. Kaczorek, A. Dzieliński, W. Dąbrowski, and R. Łopatka, Principles of Control Theory, WNT, Warszawa, 2009, (in Polish).
[33] G.G. Rigatos, “A derivative-free Kalman filtering approach to state estimation-based control of nonlinear systems, IEEE Trans. on Industrial Electronics 59 (10), 3987-3997 (2012).
[34] D. Simon, “Kalman filtering with state constraints: a survey of linear and nonlinear algorithms”, Control Theory & Applications 4 (8), 1303-1318 (2010).
[35] C.M. Kwan and F.L. Lewis, “A note on Kalman filtering”, IEEE Trans. on Education 42 (3), 225-227 (1999).
[36] B. Teixeira, J. Chandrasekar, H.J. Palanthandalam-Madapusi, L. Torres, L.A. Aguirre, and D.S. Bernstein, “Gain-constrained Kalman filtering for linear and nonlinear systems”, IEEE Trans. on Signal Processing 56 (9), 4113-4123 (2008).
[37] J. Korbicz, M. Witczak, and V. Puig, “LMI-based strategies for designing observers and unknown input observers for nonlinear discrete-time systems”, Bull. Pol. Ac.: Tech. 55 (1), 31-42 (2007).
[38] K. Szabat, T. Orłowska-Kowalska, and K.P. Dyrcz, “Extended Kalman filters in the control structure of two-mass drive system”, Bull. Pol. Ac.: Tech. 54 (3), 315-325 (2006).
[39] T. Orłowska-Kowalska, M. Kamiński, and K. Szabat, “Mechanical state variable estimation of drive system with elastic coupling using optimised feed-forward neural networks”, Bull.Pol. Ac.: Tech. 56 (3), 239-246 (2008).
[40] Z. Chen, “Bayesian filtering: from Kalman filters to particle filters, and beyond”, Statistics 182 (1), 1-69 (2003).
[41] S. Saha and F. Gustafsson, “Particle filtering with dependent noise processes”, IEEE Trans. on Signal Processing 60 (9), 4497-4508 (2012).
[42] H.A.P. Blom and E.A. Bloem, “Exact Bayesian and particle filtering of stochastic hybrid systems”, IEEE Trans.s on Aerospace and Electronic Systems 43 (1), 55-70 (2007).
[43] P.M. Djuric, J.H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M.F. Bugallo, and J. Miguez, “Particle filtering”, IEEE Signal Processing Magazine 20 (5), 19-38 (2003).
[44] D. Yee, J.P. Reilly, T. Kirubarajan, and K. Punithakumar, “Approximate conditional mean particle filtering for linear/ nonlinear dynamic state space models”, IEEE Trans. on Signal Processing 56 (12), 5790-5803 (2008).
[45] M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking”, IEEE Trans. on Signal Processing 50 (2), 174-188 (2002).
[46] K. Jaskot and A. Babiarz, “The inertial measurement unit for detection of position”, Electrical Engineering Review 86 (11a), 323-333 (2010).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-6f483c4c-3f88-4743-821e-fa3ac2261fea