A constrained Extended Kalman Filter for dynamically consistent inverse kinematics and inertial parameters identification

This paper presents a method for the real-time determination of joint angles, velocities, accelerations and joint torques of a human. The proposed method is based on a constrained Extended Kalman Filter that combines stereophotogrammetric and dynamometric data. In addition to the joint variables, subject-specific segment lengths and inertial parameters are identified. Constraints are added to the filter, by restricting the optimal Kalman gain, in order to obtain physically consistent parameters. An optimal tuning procedure of the filter's gains and a sensitivity analysis is presented. The method is validated in the plane on four human subjects and shows very good tracking of skin markers with a RMS difference lower than 15 mm. External ground reaction forces and resultant moment are also accurately estimated with an RMS difference below 3 N and 6 N.m, respectively.

[1]  F. Colloud,et al.  Estimating joint kinematics of a whole body chain model with closed-loop constraints , 2013, Multibody System Dynamics.

[2]  Raphael Andreas Hauser,et al.  Kalman Filtering with Equality and Inequality State Constraints , 2007, 0709.2791.

[3]  C. Mazzà,et al.  A least-squares identification algorithm for estimating squat exercise mechanics using a single inertial measurement unit. , 2012, Journal of biomechanics.

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

[5]  B. Galna,et al.  Quantification of soft tissue artifact in lower limb human motion analysis: a systematic review. , 2010, Gait & posture.

[6]  A Leardini,et al.  Position and orientation in space of bones during movement: anatomical frame definition and determination. , 1995, Clinical biomechanics.

[7]  L. Chèze,et al.  Adjustments to McConville et al. and Young et al. body segment inertial parameters. , 2007, Journal of biomechanics.

[8]  Gentiane Venture,et al.  Fast Determination of the Planar Body Segment Inertial Parameters Using Affordable Sensors , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Jaco F Schutte,et al.  Determination of patient-specific multi-joint kinematic models through two-level optimization. , 2005, Journal of biomechanics.

[10]  Silvio Lorenzetti,et al.  Soft Tissue Artefacts of the Human Back: Comparison of the Sagittal Curvature of the Spine Measured Using Skin Markers and an Open Upright MRI , 2014, PloS one.

[11]  R Dumas,et al.  Influence of the 3D inverse dynamic method on the joint forces and moments during gait. , 2007, Journal of biomechanical engineering.

[12]  Gentiane Venture,et al.  Identifiability and identification of inertial parameters using the underactuated base-link dynamics for legged multibody systems , 2014, Int. J. Robotics Res..

[13]  M. Damsgaard,et al.  Kinematic analysis of over-determinate biomechanical systems , 2009, Computer methods in biomechanics and biomedical engineering.

[14]  J J O'Connor,et al.  Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints. , 1999, Journal of biomechanics.

[15]  D. Simon.  Kalman filtering with state constraints: a survey of linear and nonlinear algorithms , 2010 .

[16]  Wisama Khalil,et al.  SYMORO+: A system for the symbolic modelling of robots , 1997, Robotica.

[17]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[18]  John A. Marchant,et al.  Controllability and Observability: Tools for Kalman Filter Design , 1998, BMVC.

[19]  Dana Kulic,et al.  Constrained dynamic parameter estimation using the Extended Kalman Filter , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  Morgan Sangeux,et al.  Quantification of pelvic soft tissue artifact in multiple static positions. , 2014, Gait & posture.

[21]  Aurelio Cappozzo,et al.  An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking. , 2012, Gait & posture.

[22]  G Ferrigno,et al.  Kinematical models to reduce the effect of skin artifacts on marker-based human motion estimation. , 2005, Journal of biomechanics.