Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning

Egle F, Di Domenico D, Marinelli A, Boccardo N, Canepa M, Laffranchi M, De Michieli L, Castellini C (2023)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2023

Publisher: IEEE

Event location: Singapur SG

DOI: 10.1109/ICORR58425.2023.10304813

Abstract

Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change. Incremental learning, on the other hand, reduces sources of error by quickly updating the model on new data rather than training the control model from scratch. In this study, we present an initial evaluation of a position and a velocity control strategy for simultaneous and proportional control over 3-DoFs based on incremental learning. The proposed controls are tested using a virtual Hannes prosthesis on two healthy participants. The performances are evaluated over eight sessions by performing the Target Achievement Control test and administering SUS and NASA-TLX questionnaires. Overall, this preliminary study demonstrates that both control strategies are promising approaches for prosthetic control, offering the potential to improve the usability of prostheses for individuals with limb loss. Further research extended to a wider population of both healthy subjects and amputees will be essential to thoroughly assess these control paradigms.

Authors with CRIS profile

Fabio Egle Assistive Intelligent Robotics Lab Claudio Castellini Assistive Intelligent Robotics Lab

Involved external institutions

Italian Institute of Technology / Istituto Italiano di Tecnologia (IIT) IT Italy (IT)

How to cite

APA:

Egle, F., Di Domenico, D., Marinelli, A., Boccardo, N., Canepa, M., Laffranchi, M.,... Castellini, C. (2023). Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning. In Proceedings of the 2023 International Conference on Rehabilitation Robotics (ICORR). Singapur, SG: IEEE.

MLA:

Egle, Fabio, et al. "Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning." Proceedings of the 2023 International Conference on Rehabilitation Robotics (ICORR), Singapur IEEE, 2023.

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