Publications featured in gravity Volume 2, Issue 1 (March 19, 2022)
A number of exciting papers have been published this winter in the field of dance science. Here are just a few highlights that the LAB:SYNC team wanted to share:
Arpitha, D., Balasubrahmanyam, M., & Kumar, D. A. (2022). Depth based Indian Classical Dance Mudra’s Recognition using Support Vector Machine. 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 885–888. https://doi.org/10.1109/ICSSIT53264.2022.9716486
Beune, E., Muilwijk, M., Jelsma, J., van Valkengoed, I., Teitsma-jansen, A., Kumar, B., Diaz, E., Gill, J., Jenum, A., Palaniappan, L., van der Ploeg, H., Sheikh, A., Davidson, E., Stronks, K., & Carels, V. (2022). The acceptability and effect of a culturally-tailored dance intervention to promote physical activity in women of South Asian origin at risk of diabetes in the Netherlands—A mixed-methods feasibility study. PLoS ONE, 17(2). https://doi.org/10.1371/journal.pone.0264191
Cameron, K. L., McDonald, C. E., Allison, K., McGinley, J. L., Cheong, J. L., & Spittle, A. J. (2022). Acceptability of Dance PREEMIE (a Dance PaRticipation intervention for Extremely prEterm children with Motor Impairment at prEschool age) from the perspectives of families and dancer teachers: a reflexive thematic analysis. Physiotherapy Theory and Practice, 1–13. https://doi.org/10.1080/09593985.2022.2036280
Carapellotti, A. M., Rodger, M., & Doumas, M. (2022). Evaluating the effects of dance on motor outcomes, non-motor outcomes, and quality of life in people living with Parkinson’s: a feasibility study. Pilot and Feasibility Studies, 8(1), 36. https://doi.org/10.1186/s40814-022-00982-9
DeJesus, B., Passos, A., Menezes, E., Adorno, E., DeSantana JM, & Teixeira-Machado L. (2021). Incorporation of Dance Practice into the Lifestyle of People with Disabilities and Its Impact on Quality of Life Over Time. Physical Medicine and Rehabilitation – International, 8(2), 1–8.
Dias Pereira dos Santos, A., Loke, L., Yacef, K., & Martinez-Maldonado, R. (2022). Enriching teachers’ assessments of rhythmic Forró dance skills by modelling motion sensor data. International Journal of Human-Computer Studies, 161, 102776. https://doi.org/https://doi.org/10.1016/j.ijhcs.2022.102776
Gates, P., Discenzo, F. M., Kim, J. H., Lemke, Z., Meggitt, J., & Ridgel, A. L. (2022). Analysis of Movement Entropy during Community Dance Programs for People with Parkinsons Disease and Older Adults: A Cohort Study. International Journal of Environmental Research and Public Health, 19(2). https://doi.org/10.3390/ijerph19020655
Jisha Raj, R., Dharan, S., & Sunil, T. T. (2021). Dimensionality Reduction and Visualization of Bharatanatyam Mudras. International Journal of Image and Graphics, 2350001. https://doi.org/10.1142/S0219467823500018
Lei, Y., Li, X., & Chen, Y. J. (2022). Dance Evaluation Based on Movement and Neural Network. Journal of Mathematics, 2022, 6968852. https://doi.org/10.1155/2022/6968852
Liu, Y., Fan, M., & Xu, W. (2021). Recognition method of dance rotation based on multi-feature fusion. International Journal of Arts and Technology, 13(2), 91–107. https://doi.org/10.1504/IJART.2021.120596
Luo, W., Ning, B. (2022). High-Dynamic Dance Motion Recognition Method Based on Video Visual Analysis. Scientific Programming, 2022, 6724892. https://doi.org/10.1155/2022/6724892
Lyu, Y., & Teng, L. (2021). Parallax information fusion-based for dance moving image posture extraction. ICST Transactions on Scalable Information Systems, 1–10. https://doi.org/10.4108/eai.16-12-2021.172437
Malviya, S., Zupan, B., & Meredith, P. (2022). Evidence of religious/spiritual singing and movement in mental health: A systematic review. Complementary Therapies in Clinical Practice, 47, 101567. https://doi.org/https://doi.org/10.1016/j.ctcp.2022.101567
Pappas, I. A., Monastiridi, S. G., Argiriadou, E., & Lourenço, C. C. v. (2022). Assessment of Physical Activity Using Pedometers in a Structured Greek Traditional Dance Session in Adults: A Pilot Study. Journal of Advances in Sports and Physical Education, 5(2), 16–23. https://doi.org/10.36348/jaspe.2022.v05i02.002
Quadrado, V., Moreira, M., Ferreira, H., & Passos, P. (2022). Sensing Technology for Assessing Motor Behavior in Ballet: A Systematic Review. Sports Medicine – Open, 8(1), 39. https://doi.org/10.1186/s40798-022-00429-8
Rodríguez, B., Paris-Garcia, F. (2022). Influence of Dance Programmes on Gait Parameters and Physical Parameters of the Lower Body in Older People: A Systematic Review. International Journal of Environmental Research and Public Health, 19(3). https://doi.org/10.3390/ijerph19031547
dos Santos, G. C., Nascimento Queiroz, J., Leal-Menezes, R., Leone Caetano, G., Teodoro, J. L., Pinto, R. S., Reischak-Oliveira, Á., & Rodrigues-Krause, J. (2021). Cardiorespiratory responses to isolated dance steps in young girls. International Journal of Performance Analysis in Sport, 1–16. https://doi.org/10.1080/24748668.2021.1981050
Shao, J., & Li, X. (2022). Design of Distance Learning System for Dance Movement Based on Wireless Network Communication Technology. Journal of Interconnection Networks, 2147008. https://doi.org/10.1142/S0219265921470083
Shen, D., Jiang, X., & Teng, L. (2021). Residual network based on convolution attention model and feature fusion for dance motion recognition. EAI Endorsed Transactions on Scalable Information Systems: Online First, 1–8. https://doi.org/10.4108/eai.16-12-2021.172434
Triana, D. D., Yudha, R. P., & Salman, I. (2021). Interatter Reliability E-Assessment-Based Dance Practice Assessment. Educational Sciences: Theory & Practice, 21(4), 155–164. https://doi.org/10.12738/jestp.2021.3.0010
Vecchi, M., Elf, P., Ueno, A., Dilmperi, A., Dennis, C., & Devereux, L. (2022). EXPRESS: Shall We Dance? Recreational Dance, Well-Being and Productivity Performance During COVID-19: A Three-Country Study. Journal of International Marketing, 1069031X221079609. https://doi.org/10.1177/1069031X221079609
Wang, Z. (2022). Real-Time Dance Posture Tracking Method Based on Lightweight Network. Wireless Communications and Mobile Computing, 2022, 5001896. https://doi.org/10.1155/2022/5001896
Xu, Y. (2022). Deep Learning for Dance Teaching System. In J. C. Hung, J.-W. Chang, Y. Pei, & W.-C. Wu (Eds.), Innovative Computing (pp. 1577–1581). Springer Singapore.
Yao, J., & Chen, Y. (2022). A Motion Capture Data-Driven Automatic Labanotation Generation Model Using the Convolutional Neural Network Algorithm. Wireless Communications and Mobile Computing, 2022, 2618940. https://doi.org/10.1155/2022/2618940
Yao, P. (2022). Key Frame Extraction Method of Music and Dance Video Based on Multicore Learning Feature Fusion. Scientific Programming, 2022, 9735392. https://doi.org/10.1155/2022/9735392.
Zhang, R. (2022). Analyzing body changes of high-level dance movements through biological image visualization technology by convolutional neural network. J Supercomput (2022). https://doi.org/10.1007/s11227-021-04298-y
Zhu, Y. (2022). Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining. Security and Communication Networks, 2022, 8176863. https://doi.org/10.1155/2022/8176863
Zinelabidine, K., Elghoul, Y., Jouira, G., & Sahli, S. (2021). The Effect of an 8-Week Aerobic Dance Program on Executive Function in Children. Perceptual and Motor Skills, 129(1), 153–175. https://doi.org/10.1177/00315125211058001