Posture and sequence recognition for Bharatanatyam dance performances using machine learning approaches

Mallick, Tanwi; Das, Partha Pratim; Majumdar, A. K.

doi:10.1016/j.jvcir.2022.103548

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…The author concluded that using machine learning achieves very good results with high accuracy. Comparing three different algorithms, it was observed that deep learning, part of machine learning, obtains data with the highest accuracy [18]. The same conclusion was reached by another author who studied the posture during prayer for people of Islamic ethnicity [19].…”

Section: Discussionsupporting

confidence: 60%

“…Using the Gaussian Mixture Model, an accuracy of 83.04% was obtained, while the use of the support vector machine algorithm, an accuracy of 97.95% was observed. The best result was obtained using the Convolutional Neural Network deep learning algorithm with an accuracy of 99.12% in posture recognition [18]. Koubâa, in his article published in 2019, raised the issue of correct posture during prayer.…”

Section: Resultsmentioning

confidence: 99%

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A systematic review of integrated machine learning in posture recognition

Jurjiu

Avram

Vutan

et al. 2021

Timisoara Physical Education and Rehabilitation Journal

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Introduction Posture detection is used in various situations such as medical care, surveillance, virtual environment, indoor monitoring, virtual reality for animations and entertainment. The concept of machine learning has experienced great progress in the last two decades, from a curiosity started in the laboratory to a widespread practical technology for commercial use. Objective The aim of this paper is to review the literature on the use of machine learning algorithms in the medical field for posture recognition. Material and method Articles were collected from the following databases: Google Scholar, Science Direct, PubMed and Research Gate. We included only articles that were written in English, those that were available for download in full text, published after 2010, the year in which the industrialization of the idea of artificial learning began. Articles that did not assess or recognize the posture deficiencies were excluded. Results A total of 55 articles were eligible for the study. Following the inclusion criteria, and after sorting, using the exclusion criteria, a number of 16 articles remained to be analyzed, presented and discussed. Conclusions After the analysis of the articles included in this study, it can be concluded that using machine learning we can obtain very good results with high accuracy for posture recognition.

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Section: Discussionsupporting

confidence: 60%

Section: Resultsmentioning

confidence: 99%

A systematic review of integrated machine learning in posture recognition

Jurjiu

Avram

Vutan

et al. 2021

Timisoara Physical Education and Rehabilitation Journal

View full text Add to dashboard Cite

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“…During the training process, the values of these filters will be discovered. Additionally, we must specify the network architecture, the number of filters, their sizes, and other parameters prior to the training process [ 24 , 25 ]. More image features will be extracted as the number of filters in the network increases.…”

Section: Implementation Of Dance Movement Recognition Based On Deep L...mentioning

confidence: 99%

[Retracted] Dance Action Recognition Model Using Deep Learning Network in Streaming Media Environment

2022

Journal of Environmental and Public Health

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Dance movement recognition is a video technology that has a significant impact on intelligent applications and is widely applied in many industries. In the training of intelligent dance assistants, this method can be used. Dancers’ postures can be reconstructed by taking the features out of their images. Examine and correct dancers’ postures in order to recognise their dance movements. The most crucial aspect of this technology is effectively extracting features, and deep learning is currently one of the best ways to do this for video features. In this paper, the dance movement recognition method is studied using a convolution neural network based on a deep learning network. The deep-learning-network-based convolution neural network is also used to conduct a simulation test, confirming the viability of this method for the recognition of dance movements. Due to the addition of manually extracted time-domain optical flow information, the convolution neural network’s accuracy in recognising dance movements has increased by 30.65% and 19.49% for InceptionV3 and 3D-CNN networks, respectively. It is evident from this that the convolution neural network suggested in this paper is more effective at identifying dance movements. Dance movements will continue to develop quickly thanks to the improvement of the in-depth recognition system for them. This technology has a wide range of applications in the instruction and practise of dance movements, and this research has promising application potential.

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“…In addition to the above classification algorithms (SVM [72][73][74][75]43,61], GMM [76,77], HMM [70,69,78]), some other classification algorithms are used for posture recognition, such as k-nearest neighbor (k-NN) [79], random forest (RF) [80][81][82], Bayesian classification algorithm [83], decision tree (DT) [72,84,85], linear discriminant analysis [86,60], naïve Bayes (NB) [72,87], etc.…”

Section: Other Classification Approachesmentioning

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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Posture and sequence recognition for Bharatanatyam dance performances using machine learning approaches

Cited by 15 publications

References 26 publications

A systematic review of integrated machine learning in posture recognition

A systematic review of integrated machine learning in posture recognition

[Retracted] Dance Action Recognition Model Using Deep Learning Network in Streaming Media Environment

A Survey on Artificial Intelligence in Posture Recognition

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