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Analysis of the Movement Variability in Dance Activities Using Wearable Sensors

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

Abstract

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.

Keywords

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Miguel Xochicale
    • 1
  • 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

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