Biomechanical analysis of the effects of breathing techniques on dance performance and dancers’ physiological state
Abstract
This study aims to investigate the effects of different breathing techniques on the physiological state and expressive force of modern dance dancers. Here, a motion recognition model based on a Three-Dimensional Convolutional Neural Network (3D CNN) and a Transformer network is proposed to recognize dancers’ movement performance under diverse breathing patterns. The study employs high-frequency motion sensors and physiological monitoring devices, combined with questionnaires and open datasets, to collect and analyze the dancers’ heart rate, respiratory rate, muscle activation rate, and other data. The results show that under deep breathing conditions, the dancers’ heart rate reaches 0.84, significantly higher than shallow breathing (0.46) and general breathing (0.61). Furthermore, the muscle activation rate is also remarkably increased to 0.95, better than general breathing (0.73) and shallow breathing (0.58). The model proposed in this study has excellent performance on motion recognition, with an accuracy of 96.89% at 0.5 dropout, remarkably exceeding other comparison models. The study concludes that deep breathing can markedly improve the dancer’s physiological activation and performance. Moreover, the proposed model can accurately identify the correlation between breathing patterns and dancers’ movements, providing scientific support for the application of breathing techniques in dance training in the future.
References
1. Jianu A, Iorga A. The Influence of Psychokinetic Therapy on Stress and Motor Performance in Young Dancers Aged 18-19 Years. Psychology, 2024, 15(5): 825-848.
2. Gusmail S, Nugra P D, Syahrizal S. The impact of diaphragmatic breathing on the endurance of contemporary dancers at the Institut Seni Budaya Indonesia Aceh: model-based experimental design. Gelar: Jurnal Seni Budaya, 2023, 21(2): 115-127.
3. Ko K S, Lee W K. A preliminary study using a mobile app as a dance/movement therapy intervention to reduce anxiety and enhance the mindfulness of adolescents in South Korea. The Arts in Psychotherapy, 2023, 85: 102062.
4. Aljohani A. Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 2023, 15(20): 15088.
5. Wang J, Qiao L, Lv H, Lv Z. Deep transfer learning-based multi-modal digital twins for enhancement and diagnostic analysis of brain MRI image. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, 20(4): 2407-2419.
6. Harbour E, Stöggl T, Schwameder H, Finkenzeller T. Breath tools: a synthesis of evidence-based breathing strategies to enhance human running. Frontiers in Physiology, 2022, 13: 813243.
7. Virtanen N, Tiippana K, Tervaniemi M, et al. Exploring body consciousness of dancers, athletes, and lightly physically active adults. Scientific Reports, 2022, 12(1): 8353.
8. Sun X. The Importance of Breathing Training in Folk Dance Teaching. Journal of Contemporary Educational Research, 2024, 8(4): 187-192.
9. Lopes AL, Sarro KJ, Rodrigues IM, et al. Breathing Motion Pattern in Cyclists: Role of Inferior against Superior Thorax Compartment. International Journal of Sports Medicine, 2024, 45(06): 450-457.
10. Jakubovskis G, Zuša A, Solovjova J, et al. Effects of breathing exercises on young swimmers’ respiratory system parameters and performance. Molecular & Cellular Biomechanics, 2024, 21(1): 205-205.
11. Sikora M, Mikołajczyk R, Łakomy O, et al. Influence of the breathing pattern on the pulmonary function of endurance-trained athletes. Scientific Reports, 2024, 14(1): 1113.
12. Wang Y. The effectiveness of innovative technologies to manage vocal training: The knowledge of breathing physiology and conscious control in singing. Education and Information Technologies, 2024, 29(6): 7303-7319.
13. Ley D. Innovating Cross-Disciplinary Methods for Performance Voice: My Journey Applying Massage Therapy to Actor Training. Voice and Speech Review, 2023, 17(3): 369-379.
14. Gang Y, Zhang B. A Study on the Impact of Dance Flow on Individual Creative Expression. Art and Society, 2023, 2(5): 34-38.
15. Răvdan G V. Collaborative tools in the development of opera singers’ bodily. Învăţământ, Cercetare, Creaţie, 2023, 9(1): 357-368.
16. Kim SH, Shin HJ, Cho HY. Impact of Types of Breathing on Static Balance Ability in Healthy Adults. International Journal of Environmental Research and Public Health, 2022, 19(3): 1205.
17. Grissom CK, Holubkov R, Carpenter L, et al. Implementation of coordinated spontaneous awakening and breathing trials using telehealth-enabled, real-time audit and feedback for clinician adherence (TEACH): a type II hybrid effectiveness-implementation cluster-randomized trial. Implementation Science, 2023, 18(1): 45.
18. Seifert L, Létocart A, Guignard B, Regaieg MA. Effect of breathing conditions on relationships between impairment, breathing laterality and coordination symmetry in elite para swimmers. Scientific Reports, 2024, 14(1): 6456.
19. Zhang A. Optimization Simulation of Match between Technical Actions and Music of National Dance Based on Deep Learning. Mobile Information Systems, 2023, 2023(1): 1784848.
20. Parthasarathy N, Palanichamy Y. Novel video benchmark dataset generation and real-time recognition of symbolic hand gestures in Indian dance applying deep learning techniques. ACM Journal on Computing and Cultural Heritage, 2023, 16(3): 1-19.
21. Jiang H, Yan Y. Sensor based dance coherent action generation model using deep learning framework. Scalable Computing: Practice and Experience, 2024, 25(2): 1073-1090.
22. Li B, Cui W, Zhang L, et al. Difformer: Multi-resolutional differencing transformer with dynamic ranging for time series analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(11): 13586-13598.
23. Zhou Q, Li M, Zeng Q, et al. Let’s all dance: Enhancing amateur dance motions. Computational Visual Media, 2023, 9(3): 531-550.
24. Li Z. Image analysis and teaching strategy optimization of folk dance training based on the deep neural network. Scientific Reports, 2024, 14(1): 10909.
25. Jiang P, Xue Y, Neri F. Continuously evolving dropout with multi-objective evolutionary optimisation. Engineering Applications of Artificial Intelligence, 2023, 124: 106504.
26. Omar A, Abd El-Hafeez T. Optimizing epileptic seizure recognition performance with feature scaling and dropout layers. Neural Computing and Applications, 2024, 36(6): 2835-2852.
27. Chen G. An interpretable composite CNN and GRU for fine-grained martial arts motion modeling using big data analytics and machine learning. Soft Computing, 2024, 28(3): 2223-2243.
28. Li J, Gong R, Wang G. Enhancing fitness action recognition with ResNet-TransFit: Integrating IoT and deep learning techniques for real-time monitoring. Alexandria Engineering Journal, 2024, 109: 89-101.
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