Analysis of the Movement Variability in Dance Activities Using Wearable Sensors

  • Miguel XochicaleEmail author
  • Chris Baber
  • Mourad Oussalah
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 16)


Variability is an inherent feature of human movement, but little research has been done in order to measure such a characteristic using inertial sensors attached to person’s body (wearable sensors). Therefore the aim of this preliminary study is to investigate the assessment of human movement variability for dance activities. We asked thirteen participants to repeatedly dance two salsa steps (simple and complex) for 20 s. We then used a technique from nonlinear dynamics (time-delay embedding) to obtain the reconstructed state space for visual assessment of the variability of dancers. Such reconstructed state space is graphically linked with their level of skillfulness of the participants.


Empirical Mode Decomposition Inertial Sensor Deep Neural Network Wearable Sensor Gait Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Newell, K.M., Corcos, D.M. (eds.): Variability and Motor Control, 1st edn. Human Kinetics Publishers Inc., United States of America (1993)Google Scholar
  2. 2.
    Preatoni, E., Hamill, J., Harrison, A.J., Hayes, K., Emmerik, R.E.A.V., Wilson, C., Rodado, R.: Movement variability and skills monitoring in sports. Sports Biomech. 12(2), 62–92 (2013)CrossRefGoogle Scholar
  3. 3.
    Velloso, E., Bulling, A., Gellersen, H., Ugulino, W., Fuks, H.: Qualitative activity recognition of weight lifting exercises. Proceeding AH ‘13 Proceedings of the 4th Augmented Human International Conference, pp. 116–123 (2013)Google Scholar
  4. 4.
    Van Der Linden, J., Schoonderwaldt, E., Bird, J., Johnson, R.: MusicJacket—combining motion capture and vibrotactile feedback to teach violin bowing. IEEE Trans. Instrum. Meas. 60, 104–113 (2011)CrossRefGoogle Scholar
  5. 5.
    Khan, A., Mellor, S., Berlin, E., Thompson, R., McNaney, R., Olivier, P., Plotz, T.: Beyond activity recognition: skill assessment from accelerometer data. UBICOMP (2015)Google Scholar
  6. 6.
    Liao, M., Guo, Y., Qin, Y., Wang, Y.: The application of EMD in activity recognition based on a single triaxial accelerometer. Bio-Med. Mater. Eng. 6 (2015)Google Scholar
  7. 7.
    Sama, A., Ruiz, F.J., Nuria, A., Perez-Lopez, C., Catala, A., Cabestany, J.: Gait identification by means of box approximation geometry of reconstructed attractors in latent space. Neurocomputing 121, 77–88 (2013)CrossRefGoogle Scholar
  8. 8.
    Frank, J., Mannor, S., Precup, D.: Activity and gait recognition with time-delay embeddings. AAAI Conference on Artificial Intelligence, pp. 1581–1586 (2010)Google Scholar
  9. 9.
    Cao, L.: Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110, 43–50 (1997)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Miguel Xochicale
    • 1
    Email author
  • Chris Baber
    • 1
  • Mourad Oussalah
    • 2
  1. 1.School of Electronic Electrical and System EngineeringUniversity of BirminghamBirminghamUK
  2. 2.Center for Ubiquitous ComputingUniversity of OuluOuluFinland

Personalised recommendations