Dataset Information

Autoencoder-based myoelectric controller for prosthetic hands.

ABSTRACT: In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of 6.9s and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller.

SUBMITTER: Portnova-Fahreeva AA

PROVIDER: S-EPMC10331017 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Autoencoder-based myoelectric controller for prosthetic hands.

Portnova-Fahreeva Alexandra A AA Rizzoglio Fabio F Mussa-Ivaldi Ferdinando A FA Rombokas Eric E

Frontiers in bioengineering and biotechnology 20230626

In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, i ...[more]

PMID: 37434753

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Project description:BACKGROUND: Dexterous prosthetic hands that were developed recently, such as SmartHand and i-LIMB, are highly sophisticated; they have individually controllable fingers and the thumb that is able to abduct/adduct. This flexibility allows implementation of many different grasping strategies, but also requires new control algorithms that can exploit the many degrees of freedom available. The current study presents and tests the operation of a new control method for dexterous prosthetic hands. METHODS: The central component of the proposed method is an autonomous controller comprising a vision system with rule-based reasoning mounted on a dexterous hand (CyberHand). The controller, termed cognitive vision system (CVS), mimics biological control and generates commands for prehension. The CVS was integrated into a hierarchical control structure: 1) the user triggers the system and controls the orientation of the hand; 2) a high-level controller automatically selects the grasp type and size; and 3) an embedded hand controller implements the selected grasp using closed-loop position/force control. The operation of the control system was tested in 13 healthy subjects who used Cyberhand, attached to the forearm, to grasp and transport 18 objects placed at two different distances. RESULTS: The system correctly estimated grasp type and size (nine commands in total) in about 84% of the trials. In an additional 6% of the trials, the grasp type and/or size were different from the optimal ones, but they were still good enough for the grasp to be successful. If the control task was simplified by decreasing the number of possible commands, the classification accuracy increased (e.g., 93% for guessing the grasp type only). CONCLUSIONS: The original outcome of this research is a novel controller empowered by vision and reasoning and capable of high-level analysis (i.e., determining object properties) and autonomous decision making (i.e., selecting the grasp type and size). The automatic control eases the burden from the user and, as a result, the user can concentrate on what he/she does, not on how he/she should do it. The tests showed that the performance of the controller was satisfactory and that the users were able to operate the system with minimal prior training.

Project description:The functionalities of myoelectric hooks, such as whether they allow wrist movements, as well as the volume and design of the devices, may impact how fitted transradial amputees use their upper limbs. The aim of the current study was to compare two prosthetic myoelectric hooks in terms of compensatory shoulder movements, functionality and user satisfaction. This monocentric, randomized, controlled, cross-over trial evaluated eight transradial amputees fitted with two prosthetic myoelectric hooks, the Greifer and the Axon-Hook, during two consecutive periods. At the end of each period, shoulder abduction (mean and percentage of time with shoulder abduction > 60°) and manual dexterity were assessed using the Box and Blocks Test (BBT) on both sides, and satisfaction was assessed with the Evaluation of Satisfaction with Assistive Technology questionnaire. For each patient, data obtained with the BBT on the amputated side were compared with those obtained on the non-amputated side. Shoulder abduction was significantly higher with the Greifer (60.9°± 20.3°, p = 0.03) than with the Axon-Hook (39.8°± 16.9°) and also than with the NA side (37.6 ± 19.4°, p = 0.02). Shoulder abduction on the NA side (37.6 ± 19.4°) was close to that of the Axon-Hook (39.8°± 16.9°). The percentage of time spent with shoulder abduction > 60° during the BBT was higher with the Greifer than with the Axon-Hook or with the NA side (53.3 ± 34.4%, 17.6 ± 27.0% and 18.4 ± 34.9%, respectively), but the differences were not significant (p = 0.15). A significant strong negative correlation was found between shoulder abduction and wrist position with the Axon-Hook (r = -0.86; p < 0.01), but not with the Greifer. Manual dexterity and satisfaction did not differ significantly between the two devices. These results revealed compensatory movements, such as shoulder abduction in transradial amputees equipped with hooks, themselves influenced by the prosthetic device settings.

Dataset Information

Autoencoder-based myoelectric controller for prosthetic hands.

Publications

Autoencoder-based myoelectric controller for prosthetic hands.

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