A Solution for Improving Data Capture Process Aimed at Collecting Azeri Dance Data: An Action Research

Document Type : Original Articles


1 MSc Student, Faculty of Multimedia, Tabriz Islamic Art University, Tabriz, Iran

2 Assistant Professor, Faculty of Multimedia, Tabriz Islamic Art University, Tabriz, Iran

3 Professor, School of Music, Tehran University of Art, Tehran, Iran



Introduction: The modern human is on the cutting edge of information, communication, and technology, the turning point of which is to maintain the valuable traditional and cultural data inherited from the ancestors and hand down it to the descendants. This is fulfilled via stories, dances, and activities; negligence in this regard results in loss of cultural and location-based information.Materials and Methods: In this project, Perception Neuron device (a wearable system of nonoptoelectronics MoCap group) was utilized for capturing the data of the musical movements of the body. The captured data refers to the Azerbaijan region, known as Azeri Dance in the world. The study process contained three phases of possibility of identification, capturing and maintenance of musical movements on cultural-climatic backgrounds, endeavor at utilizing these musical movements of the body in creation of melodic and rhythmic patterns, and the game side of the study containing levels, game-based learning, player progress, and skill comparison among the players. The device was attached to a female performer with the body height of 165-170 cm according to software’s default body size with fewer flaws in data. The captured data was musical movements of the famous folk tune called Tərəkəmə (pronounces as /Tærækæmæ/).Results: The final product of the musical movements and Azeri dance was build and presented within a 3D room in Unity game engine. The player could move around and get closer to the performers in order to watch the dance and musical movements from different angles along with hearing the real music (perfomer danced with) syncing with the movements.Conclusion: This interdisciplinary study provided an interface for Azeri dance and rhythmic melodies using digital technology. The study can be extended to any culture from any part of the earth as well as for entertainment, medical, rehabilitation, and educational purposes.


  1. Azadehfar M. The basics of melody creation in composition. Tehran, Iran: Nashr-e Markaz Publications; 2016. p. 331. [In Persian].
  2. Jensenius AR. Action-sound: Developing methods and tools to study music-related body movement [PhD Thesis]. Oslo, Norway: Department of Musicology, University of Oslo; 2007.
  3. Kelkar R, Jensenius AR. Analyzing Free-Hand Sound-Tracings of Melodic Phrases. Appl Sci 2018, 8(1): 135.
  4. Andreas B, Robert W. Turning movement into music: Issues and applications of the MotionComposer, a therapeutic device for persons with different abilities. Sound Effects 2016; 6(1): 23-7.
  5. Visi F. Methods and technologies for the analysis and interactive use of body movements in instrumental music performance [PhD Thesis]. Plymouth, UK: University of Plymouth; 2017.
  6. Albu F, Nicolau M, Pirvan F, Hagiescu D. A Sonification Method using human body movements. Proceedings of the 10th International Conference on Creative Content Technologies; 2018 Feb 18-22; Barcelona, Spain.
  7. Lyons S, Karkou V, Roe B, Meekums B, Richards M. What research evidence is there that dance movement therapy improves the health and wellbeing of older adults with dementia? A systematic review and descriptive narrative summary. Arts Psychother 2018; 60: 32-40.
  8. Michels K, Dubaz O, Hornthal E, Bega D. "Dance Therapy" as a psychotherapeutic movement intervention in Parkinson's disease. Complement Ther Med 2018; 40: 248-52.
  9. Panagiotopoulou E. Dance therapy and the public school: The development of social and emotional skills of high school students in Greece. Arts Psychother 2018; 59: 25-33.
  10. Hachaj T, Piekarczyk M, Ogiela MR. Human actions analysis: Templates generation, matching and visualization applied to motion capture of highly-skilled karate athletes. Sensors (Basel) 2017; 17(11): 2590.
  11. Rokeby D. The construction of experience: interface as content. In: Dodsworth C, editor. Digital illusion: Entertaining the future with high technology. New York, NY: ACM Press/Addison-Wesley; 1998. p. 27-47.
  12. Kyan M, Sun G, Li H, Zhong L, Muneesawang P, Elder B, et al. An approach to ballet dance training through MS kinect and visualization in a CAVE virtual reality environment. ACM Trans Intell Syst Technol 2015; 6(2): 23.
  13. Amiri Z, Sekhavat YA, Goljaryan S. A framework for rehabilitation games to improve balance in people with multiple sclerosis (MS). Proceedings of the 2nd National and 1st International Digital Games Research Conference: Trends, Technologies, and Applications (DGRC); 2018 Nov 29-30; Tehran, Iran. p. 76-81.
  14. Schoellig A, Siegel H, Augugliaro F, D'Andrea R. So you think you can dance? Rhythmic flight performances with quadrocopters. In: LaViers A, Egerstedt M, editors. Controls and art. New York, NY: Springer; 2014. p. 73-105.
  15. Hajdin M, Kico I, Dolezal M, Chmelik J, Doulamis A, Liarokapis F. Digitization and visualization of movements of slovak folk dances. Cham, Switzerland: Springer International Publishing; 2019 p. 245-56.
  16. Bəhmənli R. Classification of Azerbaijani Folk Dances. Rast Musicology Journal 2017; 5(3): 1745-57. [In Azerbaijani].
  17. [• • Spirit of the Steppe • • •]. "Cəngi" - Azerbaijan Turkish Battle dance | Azerbaijan National Dance [Video]. YouTube [Online]. [cited 2014, Jul 25]; Available from: URL: https://www.youtube.com/watch?v=JLUk7nHyy9U
  18. Bəhmənli R. Azerbaijan Folk Dances. Baku, Azarbaijan: Adiloðlu Publications; 2002. p. 158. [In Azerbaijani].
  19. Çoban rəqsi - Ifa edir Böyükağa Məmmədov. YouTube [Online]. [cited 2020 Mar 3]. Available from: URL: https://www.youtube.com/watch?v=H2Oq6Hyye4c&feature=youtu.be
  20. Mousavi HH, Khademi M. A review on technical and clinical impact of Microsoft kinect on physical therapy and rehabilitation. J Med Eng 2014; 2014: 846514.
  21. Hong Kong Ballet. [Hong Kong Cool] “Wordless Letter” - Yuh Egami x Mike Yip x James Kong [Video]. Facebook [Online]. [cited 2018 Sep 12]; Available from: URL: https://www.facebook.com/hongkongballet/videos/269043257047270/
  22. Vignais N, Kulpa R, Brault S, Presse D, Bideau B. Which technology to investigate visual perception in sport: Video vs. virtual reality. Hum Mov Sci 2015; 39: 12-26.
  23. Stancin S, Tomazic S. Early improper motion detection in golf swings using wearable motion sensors: the first approach. Sensors (Basel) 2013; 13(6): 7505-21.
  24. Agres K, Herremans D. Music and motion-detection: a game prototype for rehabilitation and strengthening in the elderly. Proceedings of the 2017 International Conference on Orange Technologies (ICOT); 2017 Dec 8-10; Singapore, Singapore.
  25. Pfister A, West AM, Bronner S, Noah JA. Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis. J Med Eng Technol 2014; 38(5): 274-80.
  26. Rucco R, Agosti V, Jacini F, Sorrentino P, Varriale P, De Stefano M, et al. Spatio-temporal and kinematic gait analysis in patients with Frontotemporal dementia and Alzheimer's disease through 3D motion capture. Gait Posture 2017; 52: 312-7.
  27. Samaritter R, Payne H. Through the kinesthetic lens: Observation of social attunement in autism spectrum disorders. Behav Sci (Basel) 2017; 7(1): 14.
  28. Flanigan C, Manning W, Martino E. Gamified music learning system with VR force feedback for rehabilitation [BSc Thesis]. Worcester, MA: Worcester Polytechnic Institute; 2016.
  29. Kirk P, Grierson M, Bodak R, Ward N, Brander F, Kelly K, et al. Motivating stroke rehabilitation through music: A feasibility study using digital musical instruments in the home. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16); 2016 May 7-12; San Jose, CA, USA.