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Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement.

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Title: Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement.
Authors: Yu, Tianshi1 (AUTHOR) tianshiy@student.unimelb.edu.au, Mohammadi, Alireza1 (AUTHOR) alireza.mohammadi@unimelb.edu.au, Tan, Ying1 (AUTHOR) yingt@unimelb.edu.au, Choong, Peter2 (AUTHOR) pchoong@unimelb.edu.au, Oetomo, Denny2 (AUTHOR) doetomo@unimelb.edu.au
Source: IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2024, Vol. 32, p3210-3221. 12p.
Subjects: Human-robot interaction, Residual limbs, Task analysis, Virtual reality, Classification, Prosthetics
Abstract: Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Neural Systems & Rehabilitation Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 182094247
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement.
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  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Tianshi%22">Yu, Tianshi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tianshiy@student.unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Mohammadi%2C+Alireza%22">Mohammadi, Alireza</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alireza.mohammadi@unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Tan%2C+Ying%22">Tan, Ying</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yingt@unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Choong%2C+Peter%22">Choong, Peter</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> pchoong@unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Oetomo%2C+Denny%22">Oetomo, Denny</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> doetomo@unimelb.edu.au</i>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Neural+Systems+%26+Rehabilitation+Engineering%22">IEEE Transactions on Neural Systems & Rehabilitation Engineering</searchLink>. 2024, Vol. 32, p3210-3221. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Human-robot+interaction%22">Human-robot interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Residual+limbs%22">Residual limbs</searchLink><br /><searchLink fieldCode="DE" term="%22Task+analysis%22">Task analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Prosthetics%22">Prosthetics</searchLink>
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  Data: Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of IEEE Transactions on Neural Systems & Rehabilitation Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TNSRE.2024.3450973
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        Text: English
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      – SubjectFull: Classification
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      – SubjectFull: Prosthetics
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      – TitleFull: Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement.
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              Text: 2024
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