Abstract
Studies have shown that motor recovery after neurological injuries is dependent on functional reorganization. In particular, engaging muscles in skilled activities triggers a process of remodeling that could lead to improving functional outcomes. Here, we propose a novel approach for engaging targeted muscles into skilled activities while operating assistive interfaces based on wearable sensors. To enforce contribution of specific muscles to the control output of a movement-based assistive interface, we introduced a signal dependent on muscle activation as replacement of a highly correlated signal dependent on limb kinematics, as measured by a set of inertial sensors. The latter were weighted against the EMG contribution and sent as input to a linear map projecting kinematic signals onto a 2D screen. Modulation of the weighting factor allows switching from a kinematic only (assistive) to a hybrid (rehabilitative) mode by increasing or decreasing EMG contribution to the operation of the interface.
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Acknowledgements
Research supported by NIDRR grant H133E120010 and NICHD grant 1R01HD072080. Results incorporated in this manuscript have received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie, REBoT, G.A. No 750464.
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De Santis, D., Mussa-Ivaldi, F.A. (2022). Targeted Muscle Training with a Hybrid Body-Machine Interface. In: Torricelli, D., Akay, M., Pons, J.L. (eds) Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_73
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DOI: https://doi.org/10.1007/978-3-030-70316-5_73
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