End-to-end bare-hand localization system for human–computer interaction: a comprehensive analysis and viable solution

Yadav, Kuldeep; Kirupakaran, Anish Monsley; Laskar, Rabul Hussain

doi:10.1007/s00371-023-02837-7

Cited by 4 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They adopted an attention-based approach to improving the accuracy of hand semantic segmentation and gesture recognition, offering a valuable methodology for discerning subtle differences in complex dance movements. Yadav et al 8 proposed an end-to-end hand positioning system, providing a comprehensive analysis and solution for HCI. The development of this system showcased the effectiveness and flexibility of DL technology in handling complex interactive tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2024

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%