The Collection and Recognition Method of Music and Dance Movement Based on Intelligent Sensor. (3rd June 2022)

• Record Type: Journal Article
• Authors: Geng, Jun
• Other Names: Ding Baiyuan Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-06-03
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/2654892
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : With the popularization and development of Internet of Things technology, a large number of music and dance videos have emerged in all walks of life. In this information age, video communication has become a widespread communication method. In the current music and dance collection process, most of the action frame information of the dance video is repeated, and the stage background and costumes of the dance action are too many to fully express the human body movement information. Based on these problems, this article will realize the application of the intelligent sensor-based action recognition technology in the field of dance movement collection and complete the collection and recognition of music and dance movements. The research results of the article show that: (1) in the dance video image extraction process, the feature recognition effect of the proposed algorithm is the highest among the three models. The recognition effect of the upper body is 66.1, and the recognition effect of the lower body is 61.0. The image recognition effect can reach 73.4. During the statistical experiments on the recognition of different regions of the human body, the recognition effect of the intelligent sensor model proposed in the article is still the highest among the three models. The recognition effect of the upper body is 33.9, and the recognition effect of the lower body is 33.9. The recognition effect is 34.5, and the recognition effect of the whole body is 40.7. (2) In the traditional music and dance collection mode, the P values of the four test parts are all greater than 0.05, indicating that in the traditional music and dance collection mode, the differences between the four test modules are not significant. Combined with the evaluation results of the three groups in the traditional music and dance collection mode, we can conclude that under the condition that the initial conditions are basically the same, and the training conditions and environment are basically the same, the trainees who use the smart sensor music and dance collection training method are better in physical fitness. The indicators have been better improved, and the effect is greatly optimized compared with the training effect in the traditional music and dance collection mode. (3) After the test set runs, the article proposes that the accuracy rate of the dance collection model based on the smart sensor algorithm is 88.24%, the accuracy rate can reach 88.96%, the improved accuracy rate can reach 91.46%, and the accuracy rate can reach 91.79%. The ROC curve value of the article and the improved model is very stable. The ROC value before the improvement remains at about 0.90, and the ROC value after the model improvement also remains at 0.96. After the test set runs, the performance of the four models has decreased to a certain extent, but the smart sensor dance acquisition model proposed in the article has the lowest degree of decline, and the performance after the decline is still the highest among the four models. The accuracy of the model is 90.24%, and the accuracy of the improved model is 93.16%. The ROC curve values of the improved system are very stable, the ROC value has been maintained at 0.95, and the ROC value before the improvement is stable within the range of 0.85–0.95. The experimental results further illustrate that the model proposed in the article has the best performance.