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

Abstract : 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.
Type de document :
Communication dans un congrès
BioRob: Biomedical Robotics and Biomechatronics, Jun 2016, Singapour, Singapore. Institute of Electrical and Electronics Engineers - IEEE, 6th IEEE International Conference on Biomedical Robotics and Biomechatronics, pp.952-957, 2016, 〈10.1109/BIOROB.2016.7523749〉
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Soumis le : jeudi 16 août 2018 - 16:37:53
Dernière modification le : vendredi 7 septembre 2018 - 01:08:23

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Vincent Bonnet, Gauthier Daune, Vladimir Jouvok, Raphaël Dumas, Philippe Fraisse, et al.. A constrained Extended Kalman Filter for dynamically consistent inverse kinematics and inertial parameters identification. BioRob: Biomedical Robotics and Biomechatronics, Jun 2016, Singapour, Singapore. Institute of Electrical and Electronics Engineers - IEEE, 6th IEEE International Conference on Biomedical Robotics and Biomechatronics, pp.952-957, 2016, 〈10.1109/BIOROB.2016.7523749〉. 〈hal-01857576〉

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