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Sensor Selection With Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces.

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Title: Sensor Selection With Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces.
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, Denny1 (AUTHOR) doetomo@unimelb.edu.au
Source: IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2024, Vol. 31, p1732-1742. 11p.
Subjects: Linear programming, Feature extraction, Degrees of freedom, Wearable technology, Heuristic
Abstract: A Human-Prosthetic Interface (HPI) serves to estimate and realise the limb pose intended by the human user, using the information obtained from sensors worn by the user. In recent studies, the HPI maps multi-joint limb poses (i.e. coordinated movement of the body and limbs) to the inputs of multiple sensors. This is in contrast to the conventional methods where each degree of freedom of the powered prosthesis is mapped to the input of one/a pair of sensors. In this approach, it is necessary to systematically select sensors that carry the most information for the intended set of poses, to improve system accuracy and/or minimise the number of sensors, thus the complexity, in the prosthetic system. In this paper, sensor selection process is systematically formulated to maximise the information contained in the input features for a given number of sensors. Most importantly, it accounts for composite features, which are features requiring information from multiple sensors. Such composite features exist and are important in HPIs as we seek to capture coordinated motion involving movements of multiple limb and body segments. A non-convex optimisation problem is formulated which accounts for the constraint introduced by the composite features. A projection matrix is utilised as the optimisation variable to select intended features for evaluation. The problem is solved by the proposed Sensor Selection with Composite Features (SS-CF) algorithm which adapts convex-relaxation techniques. The SS-CF is benchmarked against HPI with expert-selected sensors in the literature and against a greedy heuristic method. The outcome demonstrated the efficacy of the SS-CF algorithm. [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.)
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  Data: Sensor Selection With Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces.
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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>1</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. 31, p1732-1742. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Linear+programming%22">Linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Degrees+of+freedom%22">Degrees of freedom</searchLink><br /><searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink>
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  Data: A Human-Prosthetic Interface (HPI) serves to estimate and realise the limb pose intended by the human user, using the information obtained from sensors worn by the user. In recent studies, the HPI maps multi-joint limb poses (i.e. coordinated movement of the body and limbs) to the inputs of multiple sensors. This is in contrast to the conventional methods where each degree of freedom of the powered prosthesis is mapped to the input of one/a pair of sensors. In this approach, it is necessary to systematically select sensors that carry the most information for the intended set of poses, to improve system accuracy and/or minimise the number of sensors, thus the complexity, in the prosthetic system. In this paper, sensor selection process is systematically formulated to maximise the information contained in the input features for a given number of sensors. Most importantly, it accounts for composite features, which are features requiring information from multiple sensors. Such composite features exist and are important in HPIs as we seek to capture coordinated motion involving movements of multiple limb and body segments. A non-convex optimisation problem is formulated which accounts for the constraint introduced by the composite features. A projection matrix is utilised as the optimisation variable to select intended features for evaluation. The problem is solved by the proposed Sensor Selection with Composite Features (SS-CF) algorithm which adapts convex-relaxation techniques. The SS-CF is benchmarked against HPI with expert-selected sensors in the literature and against a greedy heuristic method. The outcome demonstrated the efficacy of the SS-CF algorithm. [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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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1109/TNSRE.2023.3258225
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1732
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        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Degrees of freedom
        Type: general
      – SubjectFull: Wearable technology
        Type: general
      – SubjectFull: Heuristic
        Type: general
    Titles:
      – TitleFull: Sensor Selection With Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces.
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            NameFull: Yu, Tianshi
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            NameFull: Mohammadi, Alireza
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            NameFull: Tan, Ying
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            NameFull: Choong, Peter
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            NameFull: Oetomo, Denny
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            – D: 01
              M: 07
              Text: 2024
              Type: published
              Y: 2023
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              Value: 31
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            – TitleFull: IEEE Transactions on Neural Systems & Rehabilitation Engineering
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