Motion capture based identification of the human body inertial parameters

Identification of body inertia, masses and center of mass is an important data to simulate, monitor and understand dynamics of motion, to personalize rehabilitation programs. This paper proposes an original method to identify the inertial parameters of the human body, making use of motion capture data and contact forces measurements. It allows in-vivo painless estimation and monitoring of the inertial parameters. The method is described and then obtained experimental results are presented and discussed.

[1]  Yoshihiko Nakamura,et al.  Dynamics Identi cation of Humanoid Systems , 2008 .

[2]  Haruhisa Kawasaki,et al.  Minimum dynamics parameters of tree structure robot models , 1991, Proceedings IECON '91: 1991 International Conference on Industrial Electronics, Control and Instrumentation.

[3]  C. L. Chen,et al.  Segment inertial properties of Chinese adults determined from magnetic resonance imaging. , 2000, Clinical biomechanics.

[4]  Maxime Gautier,et al.  Numerical calculation of the base inertial parameters of robots , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[5]  Fouad Bennis,et al.  Symbolic Calculation of the Base Inertial Parameters of Closed-Loop Robots , 1995, Int. J. Robotics Res..

[6]  Jan Swevers,et al.  Optimal robot excitation and identification , 1997, IEEE Trans. Robotics Autom..

[7]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[8]  Katsu Yamane,et al.  Dynamics computation of structure-varying kinematic chains and its application to human figures , 2000, IEEE Trans. Robotics Autom..

[9]  Atsuo Kawamura,et al.  Robust biped walking with active interaction control between foot and ground , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[10]  Guangjun Liu,et al.  A base force/torque sensor approach to robot manipulator inertial parameter estimation , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[11]  Steven Dubowsky,et al.  An optimal information method for mobile manipulator dynamic parameter identification , 2003 .

[12]  Masaru Uchiyama,et al.  Moving Base Robotics and Reaction Management Control , 1996 .

[13]  Z. Moussavi,et al.  Center of Mass Approximation During Walking as a Function of Trunk and Swing Leg Acceleration , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Katsu Yamane,et al.  Dynamics Computation of Struture-Varying Kinematic Chains and Its Application to Human Figures , 1998 .

[15]  Z. Moussavi,et al.  Estimation of the Center of Bodymass During Forward Stepping using Body Acceleration , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Katsu Yamane,et al.  Natural Motion Animation through Constraining and Deconstraining at Will , 2003, IEEE Trans. Vis. Comput. Graph..

[17]  R. Jensen,et al.  Estimation of the biomechanical properties of three body types using a photogrammetric method. , 1978, Journal of biomechanics.

[18]  D. Pearsall,et al.  Inertial properties of the human trunk of males determined from magnetic resonance imaging , 1994, Annals of Biomedical Engineering.

[19]  Gentiane Venture,et al.  2P1-F09 Inertial Parameters Identifiability of Humanoid Robot Based on the Baselink Equation of Motion , 2008 .

[20]  Gentiane Venture,et al.  Modeling and Identification of Passenger Car Dynamics Using Robotics Formalism , 2006, IEEE Transactions on Intelligent Transportation Systems.