A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data

Chae, Jonghyeok; Kang, Young-Jin; Noh, Yoojeong

doi:10.3390/s20164481

Cited by 16 publications

(14 citation statements)

References 48 publications

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“…The boxplots in Figure 6 show the distribution of four F1-scores by 5-fold cross-validation [ 20 , 38 ] when classifying a class label of 10 min speed in every plantar region (i.e., T1, M1, M2, and HL). The outermost horizontal lines indicate the maximum and minimum values of the F1-score.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks

Chen

Sunardi

Liau

et al. 2021

Sensors

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Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. A wearable plantar pressure measurement system was used to measure plantar pressures during walking. An Artificial Neural Network (ANN) was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel (HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). Of the 12 participants, 10 participants (720 images) were randomly selected to train the classification model, and 2 participants (144 images) were utilized to evaluate the model performance. Experimental evaluation indicated that the ANN model effectively classified different walking speeds and durations based on the plantar region pressure images. Each plantar region pressure image (i.e., T1, M1, M2, and HL) generates different accuracies of the classification model. Higher performance was achieved when classifying walking speeds (0.8 m/s, 1.6 m/s, and 2.4 m/s) and 10 min walking duration in the T1 region, evidenced by an F1-score of 0.94. The dataset T1 could be an essential variable in machine learning to classify the walking intensity at different speeds and durations.

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Section: Discussionmentioning

confidence: 99%

“…The F1-score is a measure of a model’s accuracy in classifying datasets. This measure combines precision and recalls to represent the harmonic mean of the model [ 38 ]. The F1-score is usually used to evaluate binary or multiclass classification models on various types of machine learning and deep learning models.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks

Chen

Sunardi

Liau

et al. 2021

Sensors

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“…In the evaluation process of the proposed method, k-Fold Cross-Validation was used. It was also used to evaluate the models in many new publications [17,24]. The application of k-Fold Cross-Validation enables avoiding the model overfitting and improves the model's generalization property [24].…”

Section: Evaluation Of the Methodsmentioning

confidence: 99%

“…It was also used to evaluate the models in many new publications [17,24]. The application of k-Fold Cross-Validation enables avoiding the model overfitting and improves the model's generalization property [24]. The value of the parameter k was set at k = 10.…”

Section: Evaluation Of the Methodsmentioning

confidence: 99%

A Geometrical Divide of Data Particle in Gravitational Classification of Moons and Circles Data Sets

Rybak¹,

Dudczyk²

2020

Entropy

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Thus far, the Universal Law of Gravitation has found application in many issues related to pattern classification. Its popularity results from its clear theoretical foundations and the competitive effectiveness of the classifiers based on it. Both Moons and Circles data sets constitute distinctive types of data sets that can be found in machine learning. Despite the fact that they have not been formally defined yet, on the basis of their visualization, they can be defined as sets in which the distribution of objects of individual classes creates shapes similar to circles or semicircles. This article makes an attempt to improve the gravitational classifier that creates a data particle based on the class. The aim was to compare the effectiveness of the developed Geometrical Divide method with the popular method of creating a class-based data particle, which is described by a compound of 1 ÷ 1 cardinality in the Moons and Circles data sets classification process. The research made use of eight artificially generated data sets, which contained classes that were explicitly separated from each other as well as data sets with objects of different classes that did overlap each other. Within the limits of the conducted experiments, the Geometrical Divide method was combined with several algorithms for determining the mass of a data particle. The research did also use the k-Fold Cross-Validation. The results clearly showed that the proposed method is an efficient approach in the Moons and Circles data sets classification process. The conclusion section of the article elaborates on the identified advantages and disadvantages of the method as well as the possibilities of further research and development.

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“…If AI < 0.17, it is a hollow-foot-type deformity ( pes cavus ), and if AI > 0.28, it is a flat-feet-type deformity ( pes planus ). When 0.17 ≤ AI ≤ 0.28, it is a healthy foot without either of these two deformities [ 15 , 20 ].…”

Section: Techniquementioning

confidence: 99%

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities

et al. 2023

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Background and Objectives: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an optimal level of objectivity and reliability. The acquired results would enable an individual approach to the treatment of patients with foot deformities. Thus, the goal of this research study was the development of a new, objective model for recognizing and classifying foot deformities with the application of machine learning, by labeling baropodometric analysis data using computer vision methods. Materials and Methods: In this work, data from 91 students of the Faculty of Medicine and the Faculty of Sports and Physical Education, University of Novi Sad were used. Measurements were determined by using a baropodometric platform, and the labelling process was carried out in the Python programming language, using functions from the OpenCV library. Segmentation techniques, geometric transformations, contour detection and morphological image processing were performed on the images, in order to calculate the arch index, a parameter that gives information about the type of the foot deformity. Discussion: The foot over which the entire labeling method was applied had an arch index value of 0.27, which indicates the accuracy of the method and is in accordance with the literature. On the other hand, the method presented in our study needs further improvement and optimization, since the results of the segmentation techniques can vary when the images are not consistent. Conclusions: The labeling method presented in this work provides the basis for further optimization and development of a foot deformity classification system.

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A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data

Cited by 16 publications

References 48 publications

Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks

Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks

A Geometrical Divide of Data Particle in Gravitational Classification of Moons and Circles Data Sets

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities

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