Abstract
Controlling an assistive robotic manipulator can play a crucial role in improving lives of individuals with motor impairments. Here, we propose the use of state-of-the-art machine learning techniques for dimensionality reduction—non-linear autoencoder (AE) networks—within a Body-Machine Interface (BoMI) framework for controlling a 4D virtual manipulator. Compared to their linear counterparts, non-linear AEs allow retaining more of the original variance and spreading it more uniformly along the latent dimensions. This advantage has the potential to facilitate an effective control of devices with multiple degrees of freedom (DoFs). We tested the approach on a cohort of unimpaired participants practicing a reaching task in 3D space. As a result, all participants were able to reach a high level of control skills after training with the interface. Such findings highlight the potential of BoMIs based on non-linear AEs as a control platform for assistive manipulators.
This work was supported by the Marie Curie Integration [Grant FP7- PEOPLE-2012-CIG-334201], the Ministry of Science and Technology, Israel (Joint Israel-Italy lab in Biorobotics Artificial somatosensorial for humans and humanoids), the National Science Foundation [Grant 1632259, Grant 1823889], the NIDILRR [Grant 90REGE0005-01] and the NICHHD [Grant 5R01HD072080]
MG and FR contributed equally to this work.
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Giordano, M., Rizzoglio, F., Ballardini, G., Mussa-Ivaldi, F., Casadio, M. (2022). Controlling an Assistive Robotic Manipulator via a Non-linear 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_110
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DOI: https://doi.org/10.1007/978-3-030-70316-5_110
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