Towards online myoelectric control based on muscle synergies-to-force mapping for robotic applications

The development of a functional myoelectric control represents a big challenge within the researchers community, due to the complexity of mapping the user's movement intention onto the control signals. It is continuously gaining attention since it could be useful for building natural, intuitive and tailored human-machine interfaces. In this context, muscle synergies-based approaches are playing an important role since they may be useful to exploit the modular organization of the musculoskeletal system. Muscle synergies-based myo-control schemes have shown promising results when they are trained and validated at the same limb pose. However, dealing with a muscle-to-force mapping variability across multiple limb poses remains an open challenge, thus keeping these techniques unusable in several real application scenarios, e.g. rehabilitation contexts. In this paper, the authors propose a method able to compute the synergies-to-force mapping of a new limb pose by interpolation, with the knowledge of the synergies-to-force mapping related to a limited set of limb poses. The proposed interpolation-based approach has been evaluated on three different kind of mappings: muscle-to-force, ''Pose-Shared" synergies-to-force and ''Pose-Related" synergies-to-force. The muscle-to-force mapping considers a direct map between muscles and hand force. Both synergies-toforce approaches consider a map between muscle synergies and hand force, but, the ''Pose-Shared" mapping assumes that the muscle patterns can be factorized using data coming from different limb poses, whereas the ''Pose-Related" one assumes that each pose has its own set of muscle primitives that can be clustered together. The generalization capability of the proposed approach has been evaluated by comparing performances obtained in untrained conditions with the ones obtained in trained upper limb poses. Results showed that synergies-based approach substantially reduce the performance loss when tested on untrained upper-limb's poses, demonstrating that muscle synergies may be suitable to be shared across different working conditions. Moreover, the feasibility of the proposed approach has been preliminary tested in an online condition, demonstrating that the subject was able to accomplish the force task by controlling a virtual cursor with his muscular activations. (C) 2020 Elsevier B.V. All rights reserved.