Diabetic plantar pressure analysis using image fusion

Plantar pressure images analysis is the key issue of designing comfortable shoe products through last customizing system, which has attracted the researchers’ curiosity toward image fusion as an application of medical and industrial imaging. In the current work, image fusion has been applied using wavelet transform and compared with Laplace Pyramid. Using image fusion rules of Mean-Max, we presented a plantar pressure image fusion method employing haar wavelet transform. It was compared in different composition layers with the Laplace pyramid transform. The experimental studies deployed the haar, db2, sym4, coif2, and bior5.5 wavelet basis functions for image fusion under decomposition layers of 3, 4, and 5. Evaluation metrics were measured in the case of the different layer number of wavelet decomposition to determine the best decomposition level and to evaluate the fused image quality using with different wavelet functions. The best wavelet basis function and decomposition layers were selected through the analysis and the evaluation measurements. This study established that haar wavelet transform with five decomposition levels on plantar pressure image achieved superior performance of 89.2817% mean, 89.4913% standard deviation, 5.4196 average gradient, 14.3364 spatial frequency, 5.9323 information entropy and 0.2206 cross entropy

Learning models for semantic classification of insufficient plantar pressure images

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

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

Segmentation approaches for diabetic foot disorders

Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.This research was funded by the IACTEC Technological Training program, grant number TF INNOVA 2016–2021. This work was completed while Abián Hernández was beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Consejería de Economía, Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is partly financed by the European Social Fund (FSE) (POC 2014–2020, Eje 3 Tema Prioritario 74 (85%))

Finite element modelling of the foot for clinical application: A systematic review

Over the last two decades finite element modelling has been widely used to give new insight on foot and footwear biomechanics. However its actual contribution for the improvement of the therapeutic outcome of different pathological conditions of the foot, such as the diabetic foot, remains relatively limited. This is mainly because finite element modelling is only been used within the research domain. Clinically applicable finite element modelling can open the way for novel diagnostic techniques and novel methods for treatment planning/optimisation which would significantly enhance clinical practice. In this context this review aims to provide an overview of modelling techniques in the field of foot and footwear biomechanics and to investigate their applicability in a clinical setting. Even though no integrated modelling system exists that could be directly used in the clinic and considerable progress is still required, current literature includes a comprehensive toolbox for future work towards clinically applicable finite element modelling. The key challenges include collecting the information that is needed for geometry design, the assignment of material properties and loading on a patient-specific basis and in a cost-effective and non-invasive way. The ultimate challenge for the implementation of any computational system into clinical practice is to ensure that it can produce reliable results for any person that belongs in the population for which it was developed. Consequently this highlights the need for thorough and extensive validation of each individual step of the modelling process as well as for the overall validation of the final integrated system

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields

The Effect of Soft Tissue and Bone Morphology on the Stresses in the Foot and Ankle

The foot and ankle interface with the ground, thus they absorb reaction forces and initiate load distribution through the body. The plantar fascia (PF) is a flexible structure that absorbs reaction forces and distributes loading across the foot. It is frequently a source of foot pain especially when people have plantar fasciitis and/or diabetes mellitus. Finite element (FE) models of the foot and ankle were created to examine the function however, the plantar fascia is frequently modeled as a 1D tension only spring, which does not represent variations caused by injury and/or disease. As models move toward being patient specific, understanding what components of a model can be generic versus what should be patient specific is critical when minimizing the time to create and simulate results. The purpose of this dissertation was to develop 3D finite element foot and ankle models including different thickness of 3D solid plantar fascia (i.e., 3mm, 4mm, and 5mm) and different ankle positions (i.e., neutral position, 10° dorsiflexion, and 10° plantarflexion). Additionally, the effect of different thicknesses of cartilage (i.e., 0.5mm, 1.0mm, and 1.7mm) and bone morphology (health and injured) was investigated in a model of the talocrural joint. As the thickness of plantar fascia increased, the strains of plantar fascia were increased, and the peak plantar pressure moved from hindfoot to forefoot. Also, the peak plantar pressures were highest when the foot was in 10° of plantarflexion and lowest in the neutral position. Finally, contact area decreased with decreasing cartilage thickness, with a greater decrease in contact area in healthy ankles. In 3 models, contact stress increased as cartilage thickness decreased. The fourth model had little decrease in contact area, thus the contact pressures may have been affected more by bone morphology. In conclusion, in models of the foot and ankle, the plantar fascia can be generic if it is less than 4 mm thick, a variety of foot positions should be considered, and specific bone morphologies should be included in the ankle if there is a known pathology

Foot Arch and Plantar Pressure in the Age of 17-21 Years

Research on the plantar segment has not been widely carried out in Indonesia’s population, even though the plantar segment data will be essential in further research and therapy of plantar-related problems. Therefore, this research intends to describe the plantar profile: the foot arch and the plantar pressure difference between the right and left foot. This research applied a cross-sectional study. Subjects were recruited from the Faculty of Medicine students, Universitas Indonesia, class 2012, with inclusion criteria aged 17-21 years and normal gait. Meanwhile, the exclusion criteria consisted of having postural abnormalities, a history of neuromusculoskeletal disorders in the lower limbs, a history of fractures in the spine and legs, a history of surgery on the spine and legs, and refusing to participate in the study. Research subjects stood on a plantar scanner, conducted at the Anatomy Laboratory, the Faculty of Medicine, Universitas Indonesia. The Mann-Whitney test was then used to analyze the difference in plantar pressure between the right and left foot. The results revealed that a hundred research subjects had a proportion of a low foot arch of 4%, a normal foot arch of 89%, and a high foot arch of 7%. The median right plantar pressure was 273.5 KPa, while the median left plantar pressure was 253.5 KPa. The Mann-Whitney test showed a p-value of 0.954 for the pressure difference between right and left foot. There was no plantar pressure difference between the right and left foot