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

Abstract

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.

Competitive neural layer-based method to identify people with high risk for diabetic foot

“…A variety of methods based on AI techniques have recently been applied to diabetic foot screening. The most common ones include ANNs (20,28,29,30,31) , fuzzy logic (32,33) , and support vector machines (SVMs) (34,35) . Most systems are trained using a supervised learning approach, where an input is mapped to an output based on example input-output pairs.…”

“…Of the AI-based works, Wang et al (2017) (34) , Goyal et al. (2018) (28) , Goyal et al (2019) (30) , Li et al (2019) (33) , Wijesinghe et al (2019) (20) , and Ohura et al (2019) (31) used images to analyze diabetic foot, while Gomes et al (2015) (32) , Wang et al. (2017) (34) , Goyal et al (2018) (28) , Goyal et al (2019) (30) , Wijesinghe et al (2019) (20) , and Ohura et al (2019) (31) looked for alternatives to monitor already existing DFUs.…”

“…Different approaches based on plantar pressure include shoe designing (33) , and optical sensors (34) have also been developed for diabetic foot detection. Dynamic foot pressure measurement is necessary particularly for diabetic patients to prevent ulcers which can eventually lead to gangrene and amputation (34) .…”

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

“…if 22 i  = , then we have that, When the image is under thresholding, the selected segmentation threshold should maximize the difference between the average gray level of the foreground and the background region. The average gray level of the entire image.…”

“…It can automatically detect the center of gravity, time measured and balance of the subject, a variety of pressure impulses and other data, and can be automated analysis system. Through the analysis of Foot-Scan software, abnormalities such as cerebral palsy and diabetic foot in gait of hip, knee and ankle can be found, which provides a scientific basis for clinical early diagnosis and corrective treatment [21][22]. The parameters for sensor and all testing device are listed in Table 1.…”