Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.
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| Title: | Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. |
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| Authors: | Mathur, Neha1 neha.mathur@strath.ac.uk, Glesk, Ivan1, Buis, Arjan2 |
| Source: | Medical Engineering & Physics. Oct2016, Vol. 38 Issue 10, p1083-1089. 7p. |
| Subjects: | Skin temperature, Prosthetics, Fuzzy systems, Gaussian processes, Machine learning, Algorithms, Prediction models, Comparative studies |
| Abstract: | Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Engineering & Physics is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 118267950 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mathur%2C+Neha%22">Mathur, Neha</searchLink><relatesTo>1</relatesTo><i> neha.mathur@strath.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Glesk%2C+Ivan%22">Glesk, Ivan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Buis%2C+Arjan%22">Buis, Arjan</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Engineering+%26+Physics%22">Medical Engineering & Physics</searchLink>. Oct2016, Vol. 38 Issue 10, p1083-1089. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Skin+temperature%22">Skin temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Prosthetics%22">Prosthetics</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+systems%22">Fuzzy systems</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Engineering & Physics is the property of Elsevier B.V. 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: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.medengphy.2016.07.003 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 1083 Subjects: – SubjectFull: Skin temperature Type: general – SubjectFull: Prosthetics Type: general – SubjectFull: Fuzzy systems Type: general – SubjectFull: Gaussian processes Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Comparative studies Type: general Titles: – TitleFull: Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mathur, Neha – PersonEntity: Name: NameFull: Glesk, Ivan – PersonEntity: Name: NameFull: Buis, Arjan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 13504533 Numbering: – Type: volume Value: 38 – Type: issue Value: 10 Titles: – TitleFull: Medical Engineering & Physics Type: main |
| ResultId | 1 |
