Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design
Nowak M, Bongers RM, van der Sluis CK, Albu-Schäffer A, Castellini C (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 20
Pages Range: 39-
Journal Issue: 1
DOI: 10.1186/s12984-023-01171-2
Abstract
BACKGROUND: Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control). METHODS: The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales. RESULTS: Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study. CONCLUSIONS: Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim.
Authors with CRIS profile
Involved external institutions
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) / German Aerospace Center
Germany (DE)
University Medical Center Groningen (UMCG) / Universitair Medisch Centrum Groningen
Netherlands (NL)
Germany (DE)
University Medical Center Groningen (UMCG) / Universitair Medisch Centrum Groningen
Netherlands (NL)
How to cite
APA:
Nowak, M., Bongers, R.M., van der Sluis, C.K., Albu-Schäffer, A., & Castellini, C. (2023). Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design. Journal of neuroEngineering and rehabilitation, 20(1), 39-. https://dx.doi.org/10.1186/s12984-023-01171-2
MLA:
Nowak, Markus, et al. "Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design." Journal of neuroEngineering and rehabilitation 20.1 (2023): 39-.
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