Thermal time constant: optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes
— University of Strathclyde Skip to main navigation Skip to search Skip to main content University of Strathclyde Home Help & FAQ Home Profiles Research Units Research output Projects Datasets Equipment Student theses Impacts Prizes Activities Search by expertise, name or affiliation Thermal time constant: optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes Neha Mathur, Ivan Glesk, Adrianus Buis Electronic And Electrical Engineering Biomedical Engineering Research output: Contribution to journal › Article › peer-review 109 Downloads (Pure) Overview Fingerprint Profiles (1) Projects (1) Datasets (1) Abstract Elevated skin temperature at the body/device interface of lower-limb prostheses is one of the major factors that affect tissue health. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the hard socket and liner material employed. However, 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. To predict the residual limb temperature a machine learning algorithm - Gaussian processes is employed, which utilizes the thermal time constant values of commonly used socket and liner materials. This study highlights the relevance of thermal time constant of prosthetic materials in Gaussian Processes technique which would be useful in addressing the challenge of non-invasively monitoring the residual limb skin temperature. With the introduction of thermal time constant in the covariance function, the model can be optimized and generalized for a given prosthetic setup, thereby making the predictions more reliable. Original language English Pages (from-to) 98 – 104 Number of pages 6 Journal IET Healthcare Technology Letters Volume 3 Issue number 2 DOIs https://doi.org/10.1049/htl.2015.0023 Publication status Published - 12 Feb 2016 Keywords predictive modeling Gaussian processes lower limb prostheses skin temperature Access to Document 10.1049/htl.2015.0023Licence: CC BY 4.0 Mathur-Glesk-Buis-IETHTL2016-thermal-time-constant-optimizing-the-skin-temperature-predictive-modellingAccepted author manuscript, 1.18 MB Mathur-Glesk-Buis-IETHTL2016-thermal-time-constant-optimizing-the-skin-temperature-predictive-modellingFinal published version, 627 KBLicence: CC BY 3.0 Other files and links http://digital-library.theiet.org/content/journals/10.1049/htl.2015.0023 Profiles Arjan Buis arjan.buis strath.ac uk Biomedical Engineering - Reader Health and Wellbeing Person: Academic Projects Projects per year 2012 2012 2016 1 Finished Projects per year Epsrc Doctoral Training Grant McFarlane, A. EPSRC (Engineering and Physical Sciences Research Council) 1/10/12 → 30/09/16 Project: Research - Studentship Datasets Temperature Profile of the Residual Limb for two Trans-tibial Amputee Subjects Mathur, N. (Creator), Glesk, I. (Supervisor) & Buis, A. (Supervisor), University of Strathclyde, 12 May 2016 DOI: 10.15129/bbc73922-30c3-4890-8f97-c324ec1d33e8 Dataset Cite this APA Author BIBTEX Harvard Standard RIS Vancouver Mathur, N., Glesk, I., & Buis, A. (2016). Thermal time constant: optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes. IET Healthcare Technology Letters, 3(2), 98 – 104. https://doi.org/10.1049/htl.2015.0023 Mathur, Neha ; Glesk, Ivan ; Buis, Adrianus. / Thermal time constant : optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes. In: IET Healthcare Technology Letters. 2016 ; Vol. 3, No. 2. pp. 98 – 104. @article{244c3b4424704ac4843d9b621eccf7a7, title = "Thermal time constant: optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes", abstract = "Elevated skin temperature at the body/device interface of lower-limb prostheses is one of the major factors that affect tissue health. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the hard socket and liner material employed. However, 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. To predict the residual limb temperature a machine learning algorithm - Gaussian processes is employed, which utilizes the thermal time constant values of commonly used socket and liner materials. This study highlights the relevance of thermal time constant of prosthetic materials in Gaussian Processes technique which would be useful in addressing the challenge of non-invasively monitoring the residual limb skin temperature. With the introduction of thermal time constant in the covariance function, the model can be optimized and generalized for a given prosthetic setup, thereby making the predictions more reliable.", keywords = "predictive modeling, Gaussian processes, lower limb prostheses, skin temperature", author = "Neha Mathur and Ivan Glesk and Adrianus Buis", note = "This paper is a postprint of a paper submitted to and accepted for publication in IET Healthcare Technology Lettes and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library", year = "2016", month = feb, day = "12", doi = "10.1049/htl.2015.0023", language = "English", volume = "3", pages = "98 – 104", journal = "IET Healthcare Technology Letters", issn = "2053-3713", number = "2", } Mathur, N, Glesk, I & Buis, A 2016, 'Thermal time constant: optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes', IET Healthcare Technology Letters, vol. 3, no. 2, pp. 98 – 104. https://doi.org/10.1049/htl.2015.0023 Thermal time constant : optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes. / Mathur, Neha; Glesk, Ivan; Buis, Adrianus. In: IET Healthcare Technology Letters, Vol. 3, No. 2, 12.02.2016, p. 98 – 104. Research output: Contribution to journal › Article › peer-review TY - JOUR T1 - Thermal time constant T2 - optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes AU - Mathur, Neha AU - Glesk, Ivan AU - Buis, Adrianus N1 - This paper is a postprint of a paper submitted to and accepted for publication in IET Healthcare Technology Lettes and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library PY - 2016/2/12 Y1 - 2016/2/12 N2 - Elevated skin temperature at the body/device interface of lower-limb prostheses is one of the major factors that affect tissue health. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the hard socket and liner material employed. However, 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. To predict the residual limb temperature a machine learning algorithm - Gaussian processes is employed, which utilizes the thermal time constant values of commonly used socket and liner materials. This study highlights the relevance of thermal time constant of prosthetic materials in Gaussian Processes technique which would be useful in addressing the challenge of non-invasively monitoring the residual limb skin temperature. With the introduction of thermal time constant in the covariance function, the model can be optimized and generalized for a given prosthetic setup, thereby making the predictions more reliable. AB - Elevated skin temperature at the body/device interface of lower-limb prostheses is one of the major factors that affect tissue health. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the hard socket and liner material employed. However, 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. To predict the residual limb temperature a machine learning algorithm - Gaussian processes is employed, which utilizes the thermal time constant values of commonly used socket and liner materials. This study highlights the relevance of thermal time constant of prosthetic materials in Gaussian Processes technique which would be useful in addressing the challenge of non-invasively monitoring the residual limb skin temperature. With the introduction of thermal time constant in the covariance function, the model can be optimized and generalized for a given prosthetic setup, thereby making the predictions more reliable. KW - predictive modeling KW - Gaussian processes KW - lower limb prostheses KW - skin temperature UR - http://digital-library.theiet.org/content/journals/10.1049/htl.2015.0023 U2 - 10.1049/htl.2015.0023 DO - 10.1049/htl.2015.0023 M3 - Article VL - 3 SP - 98 EP - 104 JO - IET Healthcare Technology Letters JF - IET Healthcare Technology Letters SN - 2053-3713 IS - 2 ER - Mathur N, Glesk I, Buis A. Thermal time constant: optimizing the skin temperature predictive modelling in lower limb prostheses using Gaussian processes. IET Healthcare Technology Letters. 2016 Feb 12;3(2):98 – 104. https://doi.org/10.1049/htl.2015.0023 Powered by Pure, Scopus & Elsevier Fingerprint Engine™ © 2022 Elsevier B.V. We use cookies to help provide and enhance our service and tailor content. By continuing you agree to the use of cookies Log in to Pure About web accessibility Contact us
