Dynamics modeling and identification of the human-robot interface based on a lower limb rehabilitation robot

A lower limb rehabilitation robot, namely iLeg, has been developed recently. Since active exercises have been proven to be effective for neurorehabilitation and motor recovery, they are suggested to be implemented on iLeg. To this goal, patients' motion intention should be recognized. Therefore, a method based on the dynamic model of the human-robot interface (HRI) is designed to recognize the human motion intention. This paper is devoted to modeling and identifying the dynamics of the HRI. Firstly, the dynamic model of the HRI is designed by combining the dynamic models of the human leg and iLeg, where the human leg dynamic model (HLDM) is mainly concerned. By considering the motion trajectories during the rehabilitation exercises provided by iLeg, the human leg can be taken as a manipulator with two degrees of freedom; meanwhile, the joint angles and torques of the human leg can be measured indirectly by using the position and torque sensors mounted on the joints of iLeg. As a result, an 8-parameter HLDM can be designed by using the Lagrangian method. Then, the dynamic model of the HRI is identified by respectively and independently identifying the undetermined dynamic parameters of iLeg and the HLDM, where the dynamic parameters of the HLDM are mainly considered. Finally, the feasibility of the dynamic model of the HRI is validated by experiments.

[1]  P. Sajda,et al.  Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Philippe Lemoine,et al.  Identification of the payload inertial parameters of industrial manipulators , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Ming Liu,et al.  Stroke in China: epidemiology, prevention, and management strategies , 2007, The Lancet Neurology.

[5]  M. Gautier,et al.  Exciting Trajectories for the Identification of Base Inertial Parameters of Robots , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[6]  Carlos Canudas de Wit,et al.  Adaptive Friction Compensation in Robot Manipulators: Low Velocities , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[7]  Gentiane Venture,et al.  Influence of the model's degree of freedom on human body dynamics identification , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Gentiane Venture,et al.  Real-time identification and visualization of human segment parameters , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[10]  Toshio Tsuji,et al.  A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks , 2009, IEEE Transactions on Robotics.

[11]  C. Braun,et al.  Motor learning elicited by voluntary drive. , 2003, Brain : a journal of neurology.

[12]  Richard Ma,et al.  Intraspinal penetrating stab injury to the middle thoracic spinal cord with no neurologic deficit. , 2012, Orthopedics.

[13]  Roman Kamnik,et al.  Model based inertial sensing of human body motion kinematics in sit-to-stand movement , 2008, Simul. Model. Pract. Theory.

[14]  Gentiane Venture,et al.  Motion recognition from contact forces information and identification of the human body dynamics , 2010, 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.