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Analyzing body changes of high-level dance movements through biological image visualization technology by convolutional neural network

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Abstract

A research is designed to comprehensively study the dynamic trends and characteristics of dance movements recognition approaches, providing a valuable reference for the scientific research and development of dance art in China. Based on previous studies, a dance movements recognition model is proposed based on DL biological image visualization technology against the current problems in high-level dance movements. This model consists of the convolutional neural network (CNN)-based dance movements recognition algorithm that can recognize body movements, the dance movements recognition algorithm involving videos in the database, and the algorithm that analyzes image feature similarities of the dance movements. The Open Pose algorithm is adopted for human posture recognition. A high-precision human body pose movement state is finally obtained to judge the body changes of high-level dance movements through the division of human bones. Students with excellent dancing skills are invited to participate in the experiment, for whom specific dance movements are designed. The movement characteristics of bodies’ joint points are extracted and compared with standard movements demonstrated by teachers to obtain the differences between students’ movements and standard movements. The results review that the proposed dance movements recognition model based on biological video images has high accuracy. Within 6–8 s, the swing amplitude of the subjects’ left arm is quite different from the standard dance movements. The movements of the left arms, right arms, left legs, and right legs of experimental objects are quite different from the standard dance movements, which proves the model’s effectiveness. The results can provide a valuable reference for the research and development of dance in China and a practical basis for teaching dance movements.

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References

  1. Yahya N, Musa H, Ong ZY et al (2019) Classification of motor functions from electroencephalogram (EEG) signals based on an integrated method comprised of common spatial pattern and wavelet transform framework. Sensors 19(22):4878–4883

    Article  Google Scholar 

  2. Zbontar J, LeCun Y (2016) Stereo matching by training a CNN to compare image patches. J Mach Learn Res 17(1–32):2–14

    MATH  Google Scholar 

  3. Jin KH, McCann MT, Froustey E et al (2017) Deep CNN for inverse problems in imaging. IEEE Trans Image Process 26(9):4509–4522

    Article  MathSciNet  Google Scholar 

  4. Ovtcharov K, Ruwase O, Kim JY et al (2015) Accelerating deep CNNs using specialized hardware. Microsoft Research Whitepaper 2(11):1–4

    Google Scholar 

  5. Xiong H, Bairner A, Tang Z (2020) Embracing city life: physical activities and the social integration of the new generation of female migrant workers in urban China. Leis Stud 39(6):782–796

    Article  Google Scholar 

  6. Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep CNN. Knowl-Based Syst 108:42–49

    Article  Google Scholar 

  7. Chen J, Qiu J, Ahn CR et al (2017) Construction worker’s awkward posture recognition through supervised motion tensor decomposition. Autom Constr 77:67–81

    Article  Google Scholar 

  8. Huang J, Yu X, Wang Y et al (2016) An integrated wireless wearable sensor system for posture recognition and indoor localization. Sensors 16(11):1825–1836

    Article  Google Scholar 

  9. Kamala V R, MaryGladence L (2015) An optimal approach for social data analysis in Big Data. International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE, pp. 0205–0208.

  10. Yan C, Coenen F, Zhang B et al (2016) Driving posture recognition by CNNs. IET Comput Vision 10(2):103–114

    Article  Google Scholar 

  11. Nath S, Sinha S, Gladence L M, et al (2017) Health analysis of bicycle rider and security of bicycle using IoT. International Conference on Communication and Signal Processing (ICCSP). IEEE, pp. 0802–0806.

  12. Gladence L M, Sivakumar H H, Venkatesan G, et al (2017) Home and office automation system using human activity recognition. International Conference on Communication and Signal Processing (ICCSP). IEEE, pp. 0758–0762.

  13. Varol G, Laptev I, Schmid C et al (2018) Long-term temporal convolutions for action recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1510–1517

    Article  Google Scholar 

  14. Lv Z (2020) Virtual reality in the context of Internet of Things. Neural Comput Appl 32(13):9593–9602

    Article  Google Scholar 

  15. Moeskops P, Viergever MA, Mendrik AM et al (2016) Automatic segmentation of MR brain images with a CNN. IEEE Trans Med Imaging 35(5):1252–1261

    Article  Google Scholar 

  16. Anthimopoulos M, Christodoulidis S, Ebner L et al (2016) Lung pattern classification for interstitial lung diseases using a deep CNN. IEEE Trans Med Imaging 35(5):1207–1216

    Article  Google Scholar 

  17. Shafiee A, Nag A, Muralimanohar N et al (2016) ISAAC: A CNN accelerator with in-situ analog arithmetic in crossbars. ACM SIGARCH Comput Arch News 44(3):14–26

    Article  Google Scholar 

  18. Brumancia E, Samuel SJ, Gladence LM et al (2019) Hybrid data fusion model for restricted information using Dempster-Shafer and adaptive neuro-fuzzy inference (DSANFI) system. Soft Comput 23(8):2637–2644

    Article  Google Scholar 

  19. Senduran C, Gunes K, Topaloglu D et al (2018) Mitigation and treatment of pollutants from railway and highway runoff by pocket wetland system. A case study Chemosphere 204:335–343

    Google Scholar 

  20. Yang B, Duan H, Wu S et al (2019) Damage tolerance assessment of a brake unit bracket for high-speed railway welded bogie frames. Chin J Mech Eng 32(1):58–64

    Article  Google Scholar 

  21. Brisset S, Ogier M (2019) Collaborative and multilevel optimizations of a hybrid railway power substation. Int J Numer Model Electron Networks Devices Fields 32(4):e2289–e2296

    Article  Google Scholar 

  22. Koziarski M, Cyganek B (2017) Image recognition with deep neural networks in presence of noise – Dealing with and taking advantage of distortions. Int Comput-aided Eng 24(4):337–349

    Article  Google Scholar 

  23. Lin Z, Mu S, Shi A et al (2018) A novel method of maize leaf disease image identification based on a multichannel CNN. Trans ASABE 61(5):1461–1474

    Article  Google Scholar 

  24. Okugawa Y, Kubo M, Sato H et al (2019) Evaluation for the synchronization of the parade with openpose. J Robotics, Netw Artif Life 24:162–166

    Article  Google Scholar 

  25. Yoo HR, Lee BH (2019) An openpose-based child abuse decision system using surveillance video. J Korea Inst Inf Commun Eng 23(3):282–290

    Google Scholar 

  26. Tsai YS, Hsu LH, Hsieh YZ et al (2020) The real-time depth estimation for an occluded person based on a single image and openpose method. Mathematics 8(8):1333–1343

    Article  Google Scholar 

  27. Draisma J, Horobeţ E, Ottaviani G et al (2016) The euclidean distance degree of an algebraic variety. Found Comput Math 16(1):99–149

    Article  MathSciNet  Google Scholar 

  28. Naevdal G (2019) Positive bases with maximal cosine measure. Optimization Lett 13(6):1381–1388

    Article  MathSciNet  Google Scholar 

  29. Davies P, Rowe M, Brown DM et al (2020) Understanding the status of evidence in policing research: reflections from a study of policing domestic abuse[J]. Policing Soc 31(6):1–15

    Google Scholar 

  30. Lepore W, Hall BL, Tandon R (2020) The Knowledge for Change Consortium: a decolonising approach to international collaboration in capacity-building in community-based participatory research. Can J Dev Studies/Revue canadienne d’études du développement. 42(3):1–24

    Google Scholar 

  31. Coelho YL, Salomao JM, Kulitz HR (2017) Intelligent hand posture recognition system integrated to process control. IEEE Lat Am Trans 15(6):1144–1153

    Article  Google Scholar 

  32. Brünger J, Gentz M, Traulsen I et al (2020) Panoptic segmentation of individual pigs for posture recognition. Sensors 20(13):3710–3718

    Article  Google Scholar 

  33. Wu Z, Zhang J, Chen K et al (2019) Yoga posture recognition and quantitative evaluation with wearable sensors based on two-stage classifier and prior Bayesian network. Sensors 19(23):5129–5136

    Article  Google Scholar 

  34. Cai X, Gao Y, Li M et al (2016) Infrared human posture recognition method for monitoring in smart homes based on hidden Markov model. Sustainability 8(9):892–905

    Article  Google Scholar 

  35. Fang L, Liang N, Kang W et al (2020) Real-time hand posture recognition using hand geometric features and Fisher Vector. Signal Proc: Image Commun 82:115729–115737

    Google Scholar 

  36. Cheng K, Ye N, Malekian R et al (2019) In-air gesture interaction: Real time hand posture recognition using passive RFID tags. IEEE Access 7:94460–94472

    Article  Google Scholar 

  37. Vecchio A, Mulas F, Cola G (2017) Posture recognition using the interdistances between wearable devices. IEEE Sens lett 1(4):1–4

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the project of 2020 Teaching Reform Research Project of Colleges and Universities in Hunan Province “Innovative Research on Training Mode of Outstanding Dance Talents in Application-Oriented Colleges and Universities under the Background of Professional Certification” (No.HNJG-2020-0789). It was also supported by the project of Key project of Yiyang Social Science Project in 2020 "Research on Nanxian Dihuagu Dance from the Perspective of Semiotics" (No. 2020YS092).

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  1. College of Music and Dance, Hunan First Normal University, Changsha, 413000, China

    Ruizhi Zhang

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  1. Ruizhi Zhang
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Correspondence to Ruizhi Zhang.

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Zhang, R. Analyzing body changes of high-level dance movements through biological image visualization technology by convolutional neural network. J Supercomput 78, 10521–10541 (2022). https://doi.org/10.1007/s11227-021-04298-y

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