Ensemble Learning for Skeleton-Based Body Mass Index Classification

Kwon, Beom; Lee, Sang Hoon

doi:10.3390/app10217812

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…where β is a positive real parameter that determines the weight of the TPR. In other words, in (20), TPR is β times as important as PPV. If β is less than 1, more weight is applied to the PPV.…”

Section: B Performance Comparisonmentioning

confidence: 99%

Joint Swing Energy for Skeleton-Based Gender Classification

Kwon

Lee

2021

IEEE Access

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Human gender classification in digital image content has received considerable attention from researchers for various applications, such as demographic research, video surveillance systems, and forensic science. In this study, we investigate three-dimensional (3D) human skeleton-based gender classification using a novel gait feature called joint swing energy (JSE). JSE is a kinematic gait feature that represents how distant a model skeleton's body joints are from anatomical planes while walking. However, anatomical planes are conventionally obtained from single static poses rather than dynamic motion. Therefore, in this study, we further investigate a novel method for obtaining transverse, frontal, and median planes from a 3D gait sequence. Using these planes, each joint's movement can be represented by coordinates centered on a human body rather than 3D Cartesian coordinates. Using the proposed methods, we extract JSEs of body joints from 3D gait sequences. We show that JSEs are different between walking men and women and propose the use of JSEs for machine classification of human gender. To demonstrate the effectiveness of the proposed JSE model on the gender classification task, we evaluate the performance of machine learning algorithms trained using JSE on four publicly available datasets, referred to as Datasets A, B, C, and D. Dataset A includes gait sequences from 164 persons between the ages of 17 and 45. Dataset B contains gait sequences from 104 persons between the ages of 17 and 36. In Dataset C, there are gait sequences for 30 persons between the ages of 23 and 55. In Dataset D, gait sequences for 30 persons between the ages of 21 and 55 are contained. The evaluation results demonstrate that the proposed technique achieves the highest classification accuracy and outperforms existing techniques for all datasets. These results suggest that human gender can be classified by JSEs extracted from the 3D gait sequence. INDEX TERMS Feature extraction, gait, gender classification, human skeleton, machine learning.

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Section: B Performance Comparisonmentioning

confidence: 99%

Joint Swing Energy for Skeleton-Based Gender Classification

Kwon

Lee

2021

IEEE Access

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“…Endo = −0.7182 + 0.1451 X−0.00068 X 2 + 0.0000014 X 3 , ( [40]. BMI was categorized according to the World Health Organisation (WHO) [41]; 76% of women had normal body weight, whereas 13% were classified as underweight and 11% as overweight and obese (data not shown).…”

Section: Measurement Techniquesmentioning

confidence: 99%

Application of Artificial Neural Network to Somatotype Determination

2021

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Somatotype characteristics are important for the selection of sporting activities, as well as and the prevalence of several chronic diseases. Nowadays the most common method of somatotyping is the Heath–Carter method, which calculates the somatotype base on 10 anthropometric parameters. Another possibility for evaluation of somatotype gives commonly used bioelectrical impedance analysis), but the accuracy of the proposed formulas is questioned. Therefore, we aimed to investigate the possibility of applying an artificial neural network to achieve the formulas, which allow us to determine the endomorphy and mesomorphy using data on body height and weight and raw bioelectrical impedance analysis data in young women. The endomorphy (Endo), ectomorphy (Ecto), and mesomorphy (Meso) ratings were determined using artificial neural networks and the Heath–Carter method. To identify critical parameters and their degree of impact on the artificial neural network outputs, a sensitivity analysis was performed. The multi-layer perceptron MLP 4-4-1 (input: body mass index (BMI), reactance, resistance, and resting metabolic rate) for the Endo somatotype was proposed (root mean squared error (RMSE) = 0.66, χ2 = 0.66). The MLP 4-4-1 (input: BMI, fat-free mass, resistance, and total body water) for the Meso somatotype was proposed (RMSE = 0.76, χ2 = 0.87). All somatotypes (Endo, Meso and Ecto) can be calculated using MLP 2-4-3 (input: BMI and resistance) with accuracy RMSE = 0.67 and χ2 = 0.51. The bioelectrical impedance analysis and Heath–Carter method compliance was evaluated with the statistical algorithm proposed by Bland and Altman. The artificial neural network-based formulas allow us to determine the endomorphy and mesomorphy in young women’s ratings with high accuracy and agreement with the Heath–Carter method. The results of our study indicate the successful application of artificial neural network-based model in predicting the somatotype of young women. The artificial neural network model can be practically used in bioelectrical impedance analysis devices in the future.

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“…Recently, machine learning (ML) has received considerable attention as a powerful tool in various fields, such as computer vision [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ], natural language processing [ 25 , 26 , 27 ], and wireless communication [ 28 , 29 , 30 , 31 , 32 ]. Generally, ML approaches can be divided into three categories: supervised, unsupervised, and reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Accurate Path Loss Prediction Using a Neural Network Ensemble Method

Kwon,

Son

2024

Sensors

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Path loss is one of the most important factors affecting base-station positioning in cellular networks. Traditionally, to determine the optimal installation position of a base station, path-loss measurements are conducted through numerous field tests. Disadvantageously, these measurements are time-consuming. To address this problem, in this study, we propose a machine learning (ML)-based method for path loss prediction. Specifically, a neural network ensemble learning technique was applied to enhance the accuracy and performance of path loss prediction. To achieve this, an ensemble of neural networks was constructed by selecting the top-ranked networks based on the results of hyperparameter optimization. The performance of the proposed method was compared with that of various ML-based methods on a public dataset. The simulation results showed that the proposed method had clearly outperformed state-of-the-art methods and that it could accurately predict path loss.

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Ensemble Learning for Skeleton-Based Body Mass Index Classification

Cited by 4 publications

References 48 publications

Joint Swing Energy for Skeleton-Based Gender Classification

Joint Swing Energy for Skeleton-Based Gender Classification

Application of Artificial Neural Network to Somatotype Determination

Accurate Path Loss Prediction Using a Neural Network Ensemble Method

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