Human Body Posture Recognition Approaches

Ali, Mohammed A.; Hussain, Abir Jaafar; Sadiq, Ahmed T.

doi:10.14500/aro.10930

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…Year Focus [138] 2020 Monocular 3D human pose estimation [139] 2021 Monocular multi-person pose estimation [140] 2021 3D human pose estimation algorithms for markerless motion capture [141] 2021 2D multi-person pose estimation methods [142] 2021 Deep 3D human pose estimation [143] 2021 Human pose estimation and its application to action recognition [144] 2022…”

Section: Referencesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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Section: Referencesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

Analysis and Comparison of Posture Detection Using Deep Learning Algorithm

Singhai,

Gautam,

Kushwah

et al. 2023

Communications in Computer and Information Science

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Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia

Syakur,

Putra,

Rochman

et al. 2024

BIO Web Conf.

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Monitoring child development is vital in Indonesia due to its large child population and varying socio-economic and geographical conditions. Malnutrition adversely affects children's growth and development, with ongoing challenges in remote areas despite government efforts. This study addresses the need for accurate nutritional status classification to improve intervention strategies. This study applies the Support Vector Machine (SVM) classification method to analyze and classify nutritional status of toddlers using data from 473 samples collected from health centers in Bangkalan Regency. The classification includes categories such as Good Nutrition, Excess Nutrition, Obesity, and Risk of Excess Nutrition. The SVM model achieved an accuracy of 76% in predicting nutritional status.

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Human Body Posture Recognition Approaches

Cited by 3 publications

References 55 publications

A Survey on Artificial Intelligence in Posture Recognition

A Survey on Artificial Intelligence in Posture Recognition

Analysis and Comparison of Posture Detection Using Deep Learning Algorithm

Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia

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