What is SAM ?
SAM (Smart ArM) is an arm prosthesis dedicated to transhumeral (above-elbow) amputees or congenital amputees without a forearm. It offers patients intuitive and efficient control approaches, in order to extend their motor abilities and increase their autonomy when performing Activities of the Daily Living.
The goal of SAM is to draw specific attention on the complex case of the “above-the-elbow” amputation level and its particularities. Indeed, transhumeral or congenital amputees who need elbow prostheses have to deal with the complex control of at least two active joints (on which little research is conducted) in addition to the hand control. SAM also wants to promote the possibilities offered by non-invasive approaches, in reaction to the recent trends on invasive and surgical techniques (implanted electrodes, muscle reinnervation, etc.).
The SAM device that will be used at the CYBATHLON 2020 is a recent prototype designed especially for our pilot, Christophe Huchet. Christophe is a congenital right-arm amputee, born without a forearm but with a complete humerus, which required the use of an exoskeletal elbow prosthesis.
Therefore, SAM possesses two motorized degrees of freedom: an active robotized wrist rotator, and an original exoskeletal active elbow especially designed for congenital amputee (missing a forearm and an elbow joint) who usually have long arm residual limb. Moreover, SAM can be fitted with any commercially available prosthetic hand. For the CYBATHLON competition, it will be fitted with an i-Limb Quantum polydigital hand from Touch Bionics/Ossür. SAM also comprises a conventional socket (fitted with 6 myoelectric electrodes) designed by our partner IRR-UGECAM especially for Christophe.
Principal innovations and working principles:
The main innovation in SAM is not only in the hardware but rather on the embedded control architecture and the associated sensorimotor control policies that we have been working on for several years. Our innovative control strategy relies on two different control modes :
– a control based on the decoding of myoelectric contraction patterns associated with the voluntary contraction of residual muscles in arm amputees, measured with sEMG electrodes placed within the prosthesis socket.
– a control based on the body and residual limb movements of the user measured with embedded wearable inertial measurement units (IMUs). This control mode essentially relies on body compensatory strategies (see text box below), in order to coordinate some of the movements of the intermediate prosthetic joints and thus achieve faster reactions and more biomimetic gestures.
These control modes can be coupled together (some joints being controlled by one mode, while some other by the other mode) to enhance the dexterity of its wearer.
What is « Compensations-based control » ?
The SAM prototype to be used during the Cybathlon will be the first device to rely on this novel patented control approach which was recently developed at ISIR and which aims at offering an alternative to myoelectric control.
Indeed, one major problem of conventional myoelectric control is that it is not natural. Contracting muscles individually, one after the other (to control sequentially the prosthetic joints), is far from being similar to the motion control of an non-amputated subject. This is particularly true for transhumeral amputees who use muscles (traditionally biceps and triceps) that are not naturally responsible for some movements performed with the prosthesis, like the pronosupination of the wrist. Movement-based approaches aim at avoiding this limitation and propose a more intuitive and easier-to-learn control.
In addition to synergies-based approaches (using learned models of coordination between joints), we have been recently developing a novel control approach that relies instead on body compensations.
Body compensations are movements exhibited when the mobility of one or several joints is reduced. For example, trunk or shoulder motions are naturally used when it is hard to move the elbow and/or the wrist, whether it is because of an unresponsive limb prosthesis, a limb paralyzed after a stroke or a simple sprain affecting our normal motor behavior.
These compensatory motions are good indicators of the correct or incorrect position of the prosthesis. Indeed, if the elbow is not flexed/extended enough or if the hand is not well oriented, the amputated subject often corrects this with body compensations (bending down, flexing the trunk to one side, etc). With our compensations-based control, we detect and measure in real time these motions and compute the prosthesis movement required to make the subject go back to a more comfortable position, while keeping the hand well positioned or well oriented.
This results in prosthesis actions being perfectly coordinated with the body movements of its wearer, without requiring the user to send specific instructions to the prosthesis or having to learn complex control strategies. In short : the prosthesis basically decodes the natural body kinematic language of its user !
More information on our technology and expertise can be found in the following recent works from our team :
– Merad, M., de Montalivet, E., Legrand, M., Mastinu, E., Ortiz-Catalan, M., Touillet, A., … & Jarrassé, N. (2020). Assessment of an automatic prosthetic elbow control strategy using residual limb motion for transhumeral amputated individuals with socket or osseointegrated prostheses. IEEE Transactions on Medical Robotics and Bionics. PDF
– Legrand, M., de Montalivet, E., Richer, F., Jarrasse, N., & Morel, G. (2019, June). Reciprocal Kinematic Control: using human-robot dual adaptation to control upper limb assistive devices. PDF
– Legrand, M., Jarrassé, N. Richer, F. Morel, G. (2020, May) A closed-loop and ergonomic control for prosthetic wrist rotation. IEEE. Proc. International Conference on Robotics and Automation (ICRA)
– Jarrassé, N., De Montalivet, É., Richer, F., Nicol, C., Touillet, A., Martinet, N., … & De Graaf, J. B. (2018). Phantom-mobility-based prosthesis control in transhumeral amputees without surgical reinnervation: A preliminary study. Frontiers in bioengineering and biotechnology, 6, 164. PDF
– Jarrassé, N., Nicol, C., Touillet, A., Richer, F., Martinet, N., Paysant, J., & de Graaf, J. B. (2016). Classification of phantom finger, hand, wrist, and elbow voluntary gestures in transhumeral amputees with sEMG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(1), 71-80. PDF