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Performance monitoring and evaluation in dance teaching with mobile sensing technology

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Abstract

One of the most important jobs of dance teacher is to detect and estimate the performances of the students during their practices, including confirming the completion of the moves in and out of class as well as evaluating the performance . However, it would be a heavy burden for teachers who do it by themselves, and traditional assist of video techniques is quite expensive and inconvenient for ordinary learners. Therefore, in this paper, we apply sensors within the smart phone to follow the performance of dance trainers and estimate the correctness of motion gesture and rhythm management by measuring the similarity between the motion data of trainers and the standard data with a dynamic time warping based algorithm. And we propose an automatic grading system to assess users’ performances, including the comprehensive evaluation grade as well as the rhythm situation in each part. We apply our solution in the real world practice and compare its results with those that given by experts. According to the results of our experiments, this scheme is able to judge the dancing practice quite accurately and is accepted and confirmed by teachers and experts in dance field. Hopefully, it will be extended to other sports fields as well, such as aerobics and yoga.

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References

  1. Duarte R, Araújo D, Fernandes O et al (2010) Capturing complex human behaviors in representative sports contexts with a single camera. Medicina (Kaunas) 46(6):408–414

    Google Scholar 

  2. Stensrud S, Myklebust G, Kristianslund E et al (2011) Correlation between two-dimensional video analysis and subjective assessment in evaluating knee control among elite female team handball players. Br J Sports Med 45(7):589–595

    Article  Google Scholar 

  3. Barris Sian; Button Chris (2008) A review of vision-based motion analysis in sport. Sports Med 38(12):1025

    Article  Google Scholar 

  4. Wyon MA, Twitchett E, Angioi M, Clarke F, Metsios G, Koutedakis Y (2011) Time motion and video analysis of classical ballet and contemporary dance performance. Int J Sports Med 32(11):851

    Article  Google Scholar 

  5. Kim T, Park SI, Shin SY (2003) Rhythmic-motion synthesis based on motion-beat analysis. ACM Trans Graph (TOG) 22(3):392–401

    Article  Google Scholar 

  6. Graf H, Yoon SM, Malerczyk C (2010) Real-time 3D reconstruction and pose estimation for human motion analysis. In: 17th IEEE international conference on image processing (ICIP), pp 3981–3984

  7. Yungchien Chu, Jon Akins, Mita Lovalekar, Scott Tashman, Scott Lephart, Timothy Sell (2012) Validation of a video-based motion analysis technique in 3-D dynamic scapular kinematic measurements. J Biomech 45(14):p2462

    Article  Google Scholar 

  8. Sigal L, Balan AO, Black MJ (2010) Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int J Comput Vision 87(1–2):4–27

    Article  Google Scholar 

  9. Harms H, Amft O, Winkler R et al (2010) Ethos: miniature orientation sensor for wearable human motion analysis. Sensors, 2010 IEEE, pp 1037–1042

  10. Strohrmann C, Harms H, Tröster G et al (2011) Out of the lab and into the woods: kinematic analysis in running using wearable sensors. In: Proceedings of the 13th international conference on ubiquitous computing. ACM, pp 119–122

  11. Yu W, Yan H, Guo J, Bie R (2013) Patient’s motion recognition based on SOM-Decision Tree. In: Proceedings of the 8th international conference on wireless algorithms, systems, and applications, pp 252–264

  12. Yang J (2009) Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: Proceedings of the 1st international workshop on interactive multimedia for consumer electronics. ACM, pp 1–10

  13. Kurdthongmee W (2012) A self organizing map based motion classifier with an extension to fall detection problem and its implementation on a Smartphone. Applications of self-organizing maps, pp 161–179

  14. Lynch A, Majeed B, O’Flynn B et al (2005) A wireless inertial measurement system (WIMS) for an interactive dance environment. J Phys Conf Ser 15(1):95–101

    Article  Google Scholar 

  15. Ghasemzadeh H, Jafari R (2011) Physical movement monitoring using body sensor networks: a phonological approach to construct spatial decision trees. IEEE Trans Ind Inf 7(1):66–77

    Article  Google Scholar 

  16. Essid S, Alexiadis D, Tournemenne R et al (2012) An advanced virtual dance performance evaluator. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2269–2272

  17. Zappi P, Lombriser C, Stiefmeier T et al (2008) Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. Wireless sensor networks. Springer, Berlin, pp 17–33

    Google Scholar 

  18. Chunhao Wu, Tseng Yuchee (2011) Data compression by temporal and spatial correlations in a body-area sensor network: a case study in pilates motion recognition. IEEE Trans Mob Comput 10(10):1459–1472

    Article  Google Scholar 

  19. Ghasemzadeh H, Loseu V, Guenterberg E et al (2009) Sport training using body sensor networks: A statistical approach to measure wrist rotation for golf swing. In: Proceedings of the fourth international conference on body area networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

  20. Ghasemzadeh H, Jafari R (2011) Coordination analysis of human movements with body sensor networks: a signal processing model to evaluate baseball swings. IEEE Sens J 11(3):603–610

    Article  Google Scholar 

  21. Bevilacqua F, Zamborlin B, Sypniewski A et al (2010) Continuous realtime gesture following and recognition. Gesture in embodied communication and human–computer interaction. Springer, Berlin, pp 73–84

    Book  Google Scholar 

  22. Zappi P, Lombriser C, Stiefmeier T et al (2008) Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. Wireless Sensor Networks. Springer, Berlin, pp 17–33

    Google Scholar 

  23. Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. KDD Workshop 10(16):359–370

    Google Scholar 

  24. Liu J, Zhong L, Wickramasuriya J et al (2009) uWave: accelerometer-based personalized gesture recognition and its applications. Pervasive Mobile Comput 5(6):657–675

    Article  Google Scholar 

  25. Akl A, Valaee S (2010) Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, and compressive sensing. In: 2010 IEEE international conference on acoustics speech and signal processing (ICASSP), pp 2270–2273

  26. Shiratori T, Nakazawa A, Ikeuchi K (2004) Detecting dance motion structure through music analysis. In: proceedings of the sixth IEEE international conference on automatic face and gesture recognition, pp 857–862

  27. Wattanapong Kurdthongmee (2012) A self organizing map based motion classifier with an extension to fall detection problem and its implementation on a Smartphone. Applications of self-organizing maps

  28. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49

    Article  MATH  Google Scholar 

  29. Sakoe H, Chiba S (1971) A dynamic programming approach to continuous speech recognition. Proc Seventh Int Congr Acoust 3:65–69

    Google Scholar 

  30. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, San Diego

    MATH  Google Scholar 

  31. de Bruine Bruin W, Keren G (2003) Order effects in sequentially judged options due to the direction of comparison. Organ Behav Hum Decis Process 92(1):91–101

    Article  Google Scholar 

  32. de Bruine Bruin W (2006) Save the last dance II: unwanted serial position effects in figure skating judgments. Acta Psychol 123(3):299–311

    Article  Google Scholar 

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Acknowledgments

This research is sponsored by National Natural Science Foundation of China (61003225, 61171014,61272475,61371185) and the Fundamental Research Funds for the Central Universities (2013NT57), and the youth talents project of Beijing (YETP1711) and by SRF for ROCS, SEM.

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing Normal University, Beijing, China

    Yu Wei, Hongli Yan, Rongfang Bie & Shenling Wang

  2. Computer Teaching and Research Section, Capital University of Physical Education and Sports, Beijing, China

    Yu Wei

  3. Beijing Key Laboratory of IOT Information Security Technology, Institute of Information Engineering, CAS, Beijing, China

    Limin Sun

Authors
  1. Yu Wei
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  2. Hongli Yan
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  3. Rongfang Bie
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  4. Shenling Wang
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  5. Limin Sun
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Correspondence to Rongfang Bie.

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Wei, Y., Yan, H., Bie, R. et al. Performance monitoring and evaluation in dance teaching with mobile sensing technology. Pers Ubiquit Comput 18, 1929–1939 (2014). https://doi.org/10.1007/s00779-014-0799-7

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  • DOI: https://doi.org/10.1007/s00779-014-0799-7

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