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

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

Multi-Source Information Fusion Model In Rule-Based Gaussian-Shaped Fuzzy Control Inference System Incorporating Gaussian Density Function

An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems