PhD Position F/M Identification robotique des paramètres musculaires du membre supérieur chez l'humain

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The researcher will be part of the CAMIN team at INRIA.

The position will be funded by INRIA under the ANR B-IRD project.

The aim of this project is to develop new methods for upper-limb muscle parameters identification, using robotics platforms. The ambition is to tackle this still open issue using an innovative experimental setup and dedicated estimation and control algorithms developed in a unified framework.

The development of accurate and personalized musculoskeletal (MSK) models is essential for changing paradigms of rehabilitation and interactions involving assistive devices. Body segment inertial parameters and joint centers of rotation are important parameters, the identification of which has been vastly studied. One of the main remaining challenges is to identify the muscular part of these models parameters [1, 2].

In this project, we want to address muscle parameters identification thanks to a unique setup combining the computation of precise parameter-exciting trajectories [3, 4] and the use of robots as position and force sensors, in contact with the patient’s body. Their precise kinematics and embedded force sensors make them perfect experimental platforms to quantify body motions and adapt the model’s parameters based on their measurements.

We plan to develop a formulation that will combine, in a single real-time optimization problem, the estimation of the user-specific MSK parameters and the computation of the future exciting movements of the upper limb, in order to guarantee a good observability of the parameters of interest. Implemented as a combination of a sliding window estimator [5] and a nonlinear model predictive control algorithm [6], the relevance of this approach lies in the exploitation of the fact that the same dynamical system is controlled and estimated at the same time [7]. This unified formulation will ensure the sensitivity of the variables to be estimated with respect to the control variables.

Once the MSK model has been personalized and calibrated to the subject, it can be used in conjunction with a robotic device such as an exoskeleton to assist upper limb motion [8, 9] or a cobot to provide physical therapy [10].

[1] G. Valente, L. Pitto, D. Testi, A. Seth, S. L. Delp, R. Stagni, M. Viceconti, and F. Taddei, “Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?”PLoS One, vol. 9, no. 11, p. e112625, 2014.

[2] Ackland, D. C., Lin, Y. C., & Pandy, M. G. (2012). Sensitivity of model predictions of muscle function to changes in moment arms and muscle–tendon properties: a Monte-Carlo analysis. Journal of biomechanics, 45(8), 1463-1471.

[3] J. Jovic, F. Philipp, A. Escande, K. Ayusawa, E. Yoshida, A. Kheddar, and G. Venture,“Identification of dynamics of humanoids: Systematic exciting motion generation,” in2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE, 2015, pp. 2173–2179

[4] A. D. Wilson, J. A. Schultz, and T. D. Murphey, “Trajectory optimization for well-conditioned parameter estimation,”IEEE Transactions on Automation Science and Engineering, vol. 12, no. 1,pp. 28–36, 2014

[5] Ceglia, A., Bailly, F., & Begon, M. (2023). Moving horizon estimation of human kinematics and muscle forces. IEEE Robotics and Automation Letters.

[6] M. Neunert, C. De Crousaz, F. Furrer, M. Kamel, F. Farshidian, R. Siegwart, and J. Buchli,“Fast nonlinear model predictive control for unified trajectory optimization and tracking,” in 2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016, pp. 1398–1404.

[7] M. Mukadam, J. Dong, F. Dellaert, and B. Boots, “Steap: simultaneous trajectory estimationand planning,”Autonomous Robots, vol. 43, pp. 415–434, 2019

[8] Escarabajal, R. J., París, E., Petrič, T., Valera, Á., Mata, V., & Babič, J. (2023, December). Assistive Upper-Limb Control using a Novel Measure of Human Muscular Manipulability based on Force Envelopes. In 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 1-8). IEEE.

[9] Quesada, L., Verdel, D., Bruneau, O., Berret, B., Amorim, M. A., & Vignais, N. (2024). EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of in situ calibration. bioRxiv, 2024-01.

[10] Prendergast, J. M., Balvert, S., Driessen, T., Seth, A., & Peternel, L. (2021). Biomechanics aware collaborative robot system for delivery of safe physical therapy in shoulder rehabilitation. IEEE Robotics and Automation Letters, 6(4), 7177-7184.

Collaboration :

The recruited person will be in connection with other INRIA teams for the robotics experiment.

He/She will closely collaborate with a postdoc researcher already working on these topics in the team.

Main activities:

• Literature review on the calibration of muscle models
• Formalization of a theoretical framework unifying muscle parameter estimation and robot control
• Analysis of the combined human-robot workspace (force polytope + model validity), singularities and model validity
• Communication of excitatory trajectories to the participant (work on visualising instructions)
• Participation in experimentation on valid subjects
• Dissemination of results (publications and scientific communications)
  
  

• Subsidized meals
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (few days per week) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Contribution to mutual insurance (subject to conditions)

Gross Salary:

1st year : 2200 € per month

2nd and 3rd year : 2300  €per month