Missions

Mainly research and development

Activités

The aim of this project is to take the innovation from its current state (see context) of a very advanced prototype (TRL 4), to the effective deployment of this technology (i.e., its production and marketing) with a complete turnkey robotic solution, in particular (in phase 1, TRL 6 or even 7). More specifically, we used the Matlab toolbox to learn knee kinematics from the collected data. This learning can be improved and we propose in this project to investigate comprehensive AI techniques (i.e., physics guided) in order to anticipate certifications. Indeed, it is possible to build mathematical models of physical phenomena of which only a part of the parameters are identified by learning. AI is also being considered for the adjustment of the parameters of the digital components of the new instrumented knee brace. To do this, we want to inject physical knowledge into AI, including parameterized models. Basically, the knee brace has an integrated and controlled matrix of mini metal detectors (electromagnetic induction), the robots generate the data (and ground truth) used to learn the positioning of prostheses, pins, etc. inside the human body. Once the learning is done, real-time 3D visualization during movement is possible. We also want to learn certain parameters of the digital components of the on-board electronics to adjust the precision according to the orthosis or pin implanted. Even if the current trials are made for knee prostheses, we also want to extend the principle to hip and shoulder prostheses, etc.
More details can be provided in interviews and found on the project website: https://sites.google.com/view/rolkneematics/home

Compétences

Engineer / Master in AI with expertise in Robotics and/or Applied Physics
or
Engineer / Master in Applied Physics (magnetism) with expertise or interest in AI and, optionally, Robotics

Contexte de travail

As part of the state-funded robotics challenge program, we have developed a turnkey robotic solution for the learning phase of a device for measuring the real-time and three-dimensional kinematics of a prosthetic knee. This device comes in the form of a knee brace developed by the partner BoneTag https://bonetag.eu/ This knee brace can be worn by any patient implanted with a complete or partial knee prosthesis, or with metal parts (e.g. braces, screws...) following knee repair surgery. The knee brace can be used outside the hospital environment, and the movement data can be transmitted (via the internet or a secure line) to the treating physicians for prevention and post-operative diagnosis.
For more than a year, LIRMM (www.lirmm.fr) and BoneTag have been working to validate the learning of kinematics using two robotic arms for other research subjects. Each robot carries a part of the implanted knee at its endpoint (i.e., one of the two robots carries the femoral part and the other the tibial part). The idea is that the two robots, synchronized, generate a multitude of precise pre-programmed movements that emulate the movements of the implanted knee (including unlikely situations) within the knee brace. The latter emits sensor signals according to the position and spatial orientation of the two parts of the knee. Thanks to the kinematics of the two robots and precise 3D models of the offsets at the end points, the position and orientation of any prosthesis, pin or screw are precisely calculated. The sensor signals and kinematic calculations, carried out using the encoders and geometric parameters of the two robots, are the data of an AI that learns the exact kinematics of prostheses, pins or screws. These can then be implanted on any patient. The knee brace will then be able to reconstruct the kinematics of the implanted patient's knee in real time. Proofs of concept have already been conducted and an advanced version of the smart knee brace has been validated (this is a world first).

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Contraintes et risques

NA

Informations complémentaires

Part of the ROLKNEEMATICS project funded by ANR Robotics Challenge program