Exploration of deep learning models for localizing bare-hand in the practical environment

Yadav, Kuldeep; K., Anish Monsley; Laskar, Rabul Hussain; Ahmad, Naseem

doi:10.1016/j.engappai.2023.106253

Cited by 5 publications

(1 citation statement)

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“…The research results denoted that DL technology had high accuracy and effect in dance training 4 . Yadav et al 5 underscored the potential of DL technology in recognizing and tracking hand movements in complex backgrounds, providing crucial technical references and insights for this study. Yadav et al 6 discussed the application of gesture recognition technology in human–computer interaction (HCI).…”

Section: Literature Reviewmentioning

confidence: 82%

Image analysis and teaching strategy optimization of folk dance training based on the deep neural network

2024

Sci Rep

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To improve the recognition effect of the folk dance image recognition model and put forward new suggestions for teachers’ teaching strategies, this study introduces a Deep Neural Network (DNN) to optimize the folk dance training image recognition model. Moreover, a corresponding teaching strategy optimization scheme is proposed according to the experimental results. Firstly, the image preprocessing and feature extraction of DNN are optimized. Secondly, classification and target detection models are established to analyze the folk dance training images, and the C-dance dataset is used for experiments. Finally, the results are compared with those of the Naive Bayes classifier, K-nearest neighbor, decision tree classifier, support vector machine, and logistic regression models. The results of this study provide new suggestions for teaching strategies. The research results indicate that the optimized classification model shows a significant improvement in classification accuracy across various aspects such as action complexity, dance types, movement speed, dance styles, body dynamics, and rhythm. The accuracy, precision, recall, and F1 scores have increased by approximately 14.7, 11.8, 13.2, and 17.4%, respectively. In the study of factors such as different training images, changes in perspective, lighting conditions, and noise interference, the optimized model demonstrates a substantial enhancement in recognition accuracy and robustness. These findings suggest that, compared to traditional models, the optimized model performs better in identifying various dances and movements, enhancing the accuracy and stability of classification. Based on the experimental results, strategies for optimizing the real-time feedback and assessment mechanism in folk dance teaching, as well as the design of personalized learning paths, are proposed. Therefore, this study holds the potential to be applied in the field of folk dance, promoting the development and innovation of folk dance education.

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Section: Literature Reviewmentioning

confidence: 82%