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Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Li, Z., Wang, D., Dey, N., Ashour, A. S., Sherratt, S. and Shi, F. (2019) Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model. Biocybernetics and Biomedical Engineering. ISSN 0208-5216 (In Press)

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Abstract/Summary

Diabetes mellitus is a clinical syndrome caused by the interaction of genetic and environmental factors. The change of plantar pressure in diabetic patients is one of the important reasons for the occurrence of diabetic foot. The abnormal increase of plantar pressure is a predictor of the common occurrence of foot ulcers. The feature extraction of plantar pressure distribution will be beneficial to the design and manufacture of diabetic shoes that will be beneficial for early protection of Diabetes mellitus patients. In this research, texture-based features of the Angular Second Moment (ASM), Moment of Inertia (MI), Inverse Difference Monument (IDM), and Entropy (E) have been selected and fused by using the an up-down algorithm. The fused features are normalized to predict comfort plantar pressure imaging dataset using an improved Fuzzy Hidden Markov Model (FHMM). In FHMM, type-I fuzzy set is proposed and Fuzzy Baum-Welch algorithm is also applied to estimate the next features. The results are discussed, and by comparing with other back-forward algorithms and different fusion operations in FHMM. Improved HMMs with up-down fusion using type-I fuzzy definition performs high effectiveness in prediction comfort plantar pressure distribution in an image dataset with an accuracy of 82.2% and the research will be applied to the shoe-last personalized customization in the industry.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:84495
Publisher:Elsevier

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