TY - JOUR
T1 - Learning to operate a high-dimensional hand via a low-dimensional controller
AU - Portnova-Fahreeva, Alexandra A.
AU - Rizzoglio, Fabio
AU - Casadio, Maura
AU - Mussa-Ivaldi, Ferdinando A.
AU - Rombokas, Eric
N1 - Funding Information:
This project was supported by NSF-1632259, by DHHS NIDILRR Grant 90REGE0005-01-00 (COMET), by NIH/NIBIB grant 1R01EB024058-01A1, and NSF-2054406.
Publisher Copyright:
Copyright © 2023 Portnova-Fahreeva, Rizzoglio, Casadio, Mussa-Ivaldi and Rombokas.
PY - 2023
Y1 - 2023
N2 - Dimensionality reduction techniques have proven useful in simplifying complex hand kinematics. They may allow for a low-dimensional kinematic or myoelectric interface to be used to control a high-dimensional hand. Controlling a high-dimensional hand, however, is difficult to learn since the relationship between the low-dimensional controls and the high-dimensional system can be hard to perceive. In this manuscript, we explore how training practices that make this relationship more explicit can aid learning. We outline three studies that explore different factors which affect learning of an autoencoder-based controller, in which a user is able to operate a high-dimensional virtual hand via a low-dimensional control space. We compare computer mouse and myoelectric control as one factor contributing to learning difficulty. We also compare training paradigms in which the dimensionality of the training task matched or did not match the true dimensionality of the low-dimensional controller (both 2D). The training paradigms were a) a full-dimensional task, in which the user was unaware of the underlying controller dimensionality, b) an implicit 2D training, which allowed the user to practice on a simple 2D reaching task before attempting the full-dimensional one, without establishing an explicit connection between the two, and c) an explicit 2D training, during which the user was able to observe the relationship between their 2D movements and the higher-dimensional hand. We found that operating a myoelectric interface did not pose a big challenge to learning the low-dimensional controller and was not the main reason for the poor performance. Implicit 2D training was found to be as good, but not better, as training directly on the high-dimensional hand. What truly aided the user’s ability to learn the controller was the 2D training that established an explicit connection between the low-dimensional control space and the high-dimensional hand movements.
AB - Dimensionality reduction techniques have proven useful in simplifying complex hand kinematics. They may allow for a low-dimensional kinematic or myoelectric interface to be used to control a high-dimensional hand. Controlling a high-dimensional hand, however, is difficult to learn since the relationship between the low-dimensional controls and the high-dimensional system can be hard to perceive. In this manuscript, we explore how training practices that make this relationship more explicit can aid learning. We outline three studies that explore different factors which affect learning of an autoencoder-based controller, in which a user is able to operate a high-dimensional virtual hand via a low-dimensional control space. We compare computer mouse and myoelectric control as one factor contributing to learning difficulty. We also compare training paradigms in which the dimensionality of the training task matched or did not match the true dimensionality of the low-dimensional controller (both 2D). The training paradigms were a) a full-dimensional task, in which the user was unaware of the underlying controller dimensionality, b) an implicit 2D training, which allowed the user to practice on a simple 2D reaching task before attempting the full-dimensional one, without establishing an explicit connection between the two, and c) an explicit 2D training, during which the user was able to observe the relationship between their 2D movements and the higher-dimensional hand. We found that operating a myoelectric interface did not pose a big challenge to learning the low-dimensional controller and was not the main reason for the poor performance. Implicit 2D training was found to be as good, but not better, as training directly on the high-dimensional hand. What truly aided the user’s ability to learn the controller was the 2D training that established an explicit connection between the low-dimensional control space and the high-dimensional hand movements.
KW - EMG
KW - autoencoders
KW - dimensionality reduction
KW - hand
KW - kinematics
KW - learning
KW - myoelectric
KW - prosthetics
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U2 - 10.3389/fbioe.2023.1139405
DO - 10.3389/fbioe.2023.1139405
M3 - Article
C2 - 37214310
AN - SCOPUS:85159790246
SN - 2296-4185
VL - 11
JO - Frontiers in Bioengineering and Biotechnology
JF - Frontiers in Bioengineering and Biotechnology
M1 - 1139405
ER -