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

Research output: Contribution to journalArticle

23 Citations (Scopus)

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.
LanguageEnglish
Pages1083-1089
Number of pages7
JournalMedical Engineering and Physics
Volume38
Issue number10
Early online date21 Jul 2016
DOIs
Publication statusPublished - 1 Oct 2016

Fingerprint

Skin Temperature
Fuzzy inference
Prosthetics
Learning algorithms
Prostheses and Implants
Learning systems
Lower Extremity
Skin
Temperature
Fuzzy systems
Monitoring
Extremities
Temperature sensors
Machine Learning

Keywords

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

Cite this

@article{682e4a42358842f097a8f73dc825409b,
title = "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",
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.",
keywords = "ANFIS, fuzzy Logic, Gaussian process for machine learning, lower limb prosthetics, modeling, temperature",
author = "Neha Mathur and Ivan Glesk and Arjan Buis",
year = "2016",
month = "10",
day = "1",
doi = "10.1016/j.medengphy.2016.07.003",
language = "English",
volume = "38",
pages = "1083--1089",
journal = "Medical Engineering and Physics",
issn = "1350-4533",
number = "10",

}

TY - JOUR

T1 - 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

AU - Mathur, Neha

AU - Glesk, Ivan

AU - Buis, Arjan

PY - 2016/10/1

Y1 - 2016/10/1

N2 - 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.

AB - 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.

KW - ANFIS

KW - fuzzy Logic

KW - Gaussian process for machine learning

KW - lower limb prosthetics

KW - modeling

KW - temperature

UR - http://www.sciencedirect.com/science/journal/13504533

U2 - 10.1016/j.medengphy.2016.07.003

DO - 10.1016/j.medengphy.2016.07.003

M3 - Article

VL - 38

SP - 1083

EP - 1089

JO - Medical Engineering and Physics

T2 - Medical Engineering and Physics

JF - Medical Engineering and Physics

SN - 1350-4533

IS - 10

ER -