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Decoding of multi-joint movements using high-density EMG signals and a 7-DoF exoskeleton


A,  Sarasola-Sanz
Max Planck Institute for Biological Cybernetics, Max Planck Society;

N,  Irastorza-Landa
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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A, S.-S., N, I.-L., E, L.-L., G, R., N, B., & A, R.-M. (2016). Decoding of multi-joint movements using high-density EMG signals and a 7-DoF exoskeleton. Poster presented at 46th Annual Meeting of the Society for Neuroscience (Neuroscience 2016), San Diego, CA, USA.

Myoelectric decoders have been broadly proposed and used in combination with robotic exoskeletons as a tool for the rehabilitation of motor disabled patients. Most of them are limited to the classification of discrete movements and some others have shown a modest decoding performance of trajectories including very few degrees of freedom (DoFs) of the arm. Although it is still not clear to which extent the decoding performance could influence the rehabilitation effects of the therapy, it is natural to believe that a higher decoding accuracy may establish a more direct and robust link between the movement intention and the response stimuli (i.e. the feedback). In a previous study, we demonstrated the viability of a motor rehabilitation platform consisting of: i) a set of 10 surface bipolar electrodes placed over the abductor pollicis longus, extensor carpi ulnaris and digitorium, flexor carpi radialis, ulnaris and palmaris longus, pronator teres, biceps, triceps and anterior, lateral and posterior portions of the deltoid; ii) a myoelectric decoder that predicted multi-DoF kinematics based on the input EMG; iii) the IS-MORE 7-DoF robotic exoskeleton (Tecnalia, San Sebastian, Spain) that moved according to the predicted kinematics, providing the subject with visual and proprioceptive feedback. It was shown that the ridge regression was a potential algorithm for the EMG-decoding of trajectories. However, the non-optimized ergonomy of the exoskeleton and the small number of electrodes constituted a limitation to achieve a reliable and accurate EMG-control. In this study, we address those issues by optimizing the design of the fingers’ interface of the exoskeleton and by recording forearm EMG with high-density electrodes. On the whole, this improved rehabilitation system offers the following advantages: i) It allows functional movements involving proximal and distal DoFs of the arm; ii) It can continuously map the EMG into multi-DoF kinematics; iii) It provides contingent feedback, which might lead to the activation of neuroplastic mechanisms restoring motor function; iv) Even severely paralyzed patients without residual movement can utilize this system. Here, we test the decoding ability of the system in 6 healthy subjects (age: 22-29, right-handed) in one session. Participants worn the exoskeleton on their dominant arm and were asked to reach different targets while supinating and opening their hand. EMG and kinematic data was recorded and the performance of the myoelectric decoder was evaluated offline. The performance values will be presented and the potential and applicability of the presented rehabilitation system will be discussed based on the results.