SVM-based waist circumference estimation using Kinect

Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983.

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“…The analysis error was obtained at 4.62 cm from the actual measurement. It is better than manual measurement [20].…”

Section: Related Workmentioning

confidence: 92%

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Waliyansyah

Hasbullah

2021

emitter

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“…SVM. The SVM attempts to categorize data points in a multidimensional space using the proper hyperplane [45][46][47][48]. For implementing multiclass classification, a method called multiclass SVM (MSVM) has been proposed.…”

Section: Classifiermentioning

confidence: 99%

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics

Sahid,

Babar,

Uddin

2024

PLoS ONE

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Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection—filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.

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“…Four Kinect sensors were set up to conduct body measurements of 17 students according to CAESAR standard. Seo et al [17] carried out an investigation into the possibility of automatically measuring waist circumference based on SVM regression using a Kinect sensor. Bragança et al [18] conducted a validity study by comparing the Traditional approach with a 3D measuring system based on four Kinect sensors to conduct circular body measurements.…”

Section: Introductionmentioning

confidence: 99%

Investigation into the suitability of kinect sensor for automated body measurement

Ejidokun

Olasunkanmi²,

Olutayo³

et al. 2023

IJEECS

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Due to the low cost and wide availability of the Kinect sensor, researchers and experts in the field of anthropometry, sizing and clothing fiting are leveraging on its inbuilt 3D camera to develop systems for automated body measurement. This study focuses on the evaluation of the Microsoft Kinect (V1) sensor to determine its suitability for automated body measurement. The study was conducted by data collection of various body dimensions of test subjects using a measuring tape as a reference. Furthermore, a statistical approach known as the measurement system analysis was used to investigate the sensor's capability to produce accurate, reliable and consistent body measurements. The results obtained shows indicates that there exists very little variation when the measurement is repeated. Also, the instrument is relatively stable, with minimal bias which can be corrected by calibration. The outcome of the study proves the effectiveness of the Microsoft Kinect sensor as a means of conducting body measurement.

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SVM-based waist circumference estimation using Kinect

Cited by 9 publications

References 13 publications

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics

Investigation into the suitability of kinect sensor for automated body measurement

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