Hybrid sensorimotor control

Our team uses hybrid systems, which mix biological control with artificial devices, in order to (i) increase our understanding of sensorimotor control and (ii) exploit this knowledge to restore and optimize movement. Instead of being pre-programmed in the brain, movement coordination largely depends upon multiple feedback loops that operate at different levels of the sensorimotor control system. For instance, muscle mechanics provides an instantaneous functional response to small perturbations, while segmental and transcortical reflexes are able to absorb increasingly larger perturbations by precisely coordinating muscle responses for the complex musculoskeletal design of our limbs. These loops are typically violated in the case of artificial devices, such as with prosthetic limbs whose biomechanics differs from that of original limbs, and which lack sensory feedback. We use a range of closed loop hybrid systems to investigate how these lower feedback loops interact with limb’s biomechanics, how they contribute to the production of normal, coordinated movements, and how to improve the design of hybrid control strategies to restore movements.

Lab webpage

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Mots clés

Apprentissage, Codage et intégration du signal, Comportement, Connectivité, Fonctions exécutives, Formation et fonctionnement des circuits neuronaux, Handicap, Intelligence artificielle et réalité virtuelle, Interfaces humain-machine et Santé connectée, Modélisation des neurones et des réseaux de neurones, Motricité, Mouvement

Selected publications

Lento B, Segas E, Leconte V, Doat E, Danion F, Péteri R, Benois-Pineau J, de Rugy A (2024) 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality. Scientific Data, 11, 951.

Lento B, Leconte V, Bardisbanian L, Doat E, Segas E, de Rugy A (2024) Bioinspired head-to-shoulder reference frame transformation for movement-based arm prosthesis control. IEEE Robotic and Automation Letters, 9(9) 7875-7882.

Ségas E, Mick S, Leconte V, Dubois O, Klotz R, Cattaert D, de Rugy A (2023) Intuitive movement-based prosthesis control enables arm amputees to reach naturally in virtual reality. eLife 12, RP87317 https://elifesciences.org/articles/87317

Guémann M, Halgand C, Bastier A, Lansade C, Borrini L, Lapeyre E, Cattaert D, de Rugy A (2022) Sensory substitution of elbow proprioception to improve myoelectric control of upper limb prosthesis: experiment on healthy subjects and amputees. Journal of NeuroEngineering and Rehabilitation, 19 (1) 1-12.

Mick S, Segas E, Dure L, Halgand C, Benois-Pineau J, Loeb GE, Cattaert D, de Rugy A (2021) Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand. Journal of NeuroEngineering and Rehabilitation. 18:3.

Leow LA, Marinovic W, de Rugy A, Carroll T (2020) Task errors drive memories that improve sensorimotor adaptation. J Neuroscience 40 (15), 3075-3088.

Couraud M, Cattaert D, Paclet F, Oudeyer PY, de Rugy A (2018) Model and experiments to optimize co-adaptation in a simplified myoelectric control system. Journal of Neural Engineering 15(2):026006, doi: 10.1088/1741-2552/aa87cf.