Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for prediction of skin temperature in lower limb prostheses

Neha Mathur, Ivan Glesk, Arjan Buis

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35 Citations (Scopus)
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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.
Original languageEnglish
Pages (from-to)1083-1089
Number of pages7
JournalMedical Engineering and Physics
Volume38
Issue number10
Early online date21 Jul 2016
DOIs
Publication statusPublished - 1 Oct 2016

Keywords

  • ANFIS
  • fuzzy Logic
  • Gaussian process for machine learning
  • lower limb prosthetics
  • modeling
  • temperature

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  • Projects

    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

    Dataset

    Temperature Profile of the Residual Limb

    Mathur, N. (Creator), Glesk, I. (Supervisor) & Buis, A. (Supervisor), University of Strathclyde, 11 May 2016

    Dataset

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