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Article

Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study

1
Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland
2
Department of Computer, Control, and Management Engineering, La Sapienza University, 00185 Rome, Italy
3
UOS STIIMA Lecco-Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), 23900 Lecco, Italy
4
Department of Radiology, Medical Faculty, University of Geneva, 1211 Geneva, Switzerland
5
Department of Neuroscience, University of Padua, 35122 Padua, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Georg Fischer
Sensors 2021, 21(22), 7500; https://doi.org/10.3390/s21227500
Received: 24 September 2021 / Revised: 2 November 2021 / Accepted: 9 November 2021 / Published: 11 November 2021
One major challenge limiting the use of dexterous robotic hand prostheses controlled via electromyography and pattern recognition relates to the important efforts required to train complex models from scratch. To overcome this problem, several studies in recent years proposed to use transfer learning, combining pre-trained models (obtained from prior subjects) with training sessions performed on a specific user. Although a few promising results were reported in the past, it was recently shown that the use of conventional transfer learning algorithms does not increase performance if proper hyperparameter optimization is performed on the standard approach that does not exploit transfer learning. The objective of this paper is to introduce novel analyses on this topic by using a random forest classifier without hyperparameter optimization and to extend them with experiments performed on data recorded from the same patient, but in different data acquisition sessions. Two domain adaptation techniques were tested on the random forest classifier, allowing us to conduct experiments on healthy subjects and amputees. Differently from several previous papers, our results show that there are no appreciable improvements in terms of accuracy, regardless of the transfer learning techniques tested. The lack of adaptive learning is also demonstrated for the first time in an intra-subject experimental setting when using as a source ten data acquisitions recorded from the same subject but on five different days. View Full-Text
Keywords: machine learning; EMG; biofeedback; transfer learning; random forest classifier machine learning; EMG; biofeedback; transfer learning; random forest classifier
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MDPI and ACS Style

Marano, G.; Brambilla, C.; Mira, R.M.; Scano, A.; Müller, H.; Atzori, M. Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study. Sensors 2021, 21, 7500. https://doi.org/10.3390/s21227500

AMA Style

Marano G, Brambilla C, Mira RM, Scano A, Müller H, Atzori M. Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study. Sensors. 2021; 21(22):7500. https://doi.org/10.3390/s21227500

Chicago/Turabian Style

Marano, Giulio, Cristina Brambilla, Robert M. Mira, Alessandro Scano, Henning Müller, and Manfredo Atzori. 2021. "Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study" Sensors 21, no. 22: 7500. https://doi.org/10.3390/s21227500

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