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Estimating Stair Running Performance Using Inertial Sensors.
Ojeda, Lauro V; Zaferiou, Antonia M; Cain, Stephen M; Vitali, Rachel V; Davidson, Steven P; Stirling, Leia A; Perkins, Noel C.
Afiliação
  • Ojeda LV; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. lojeda@umich.edu.
  • Zaferiou AM; Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA. antonia_zaferiou@rush.edu.
  • Cain SM; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. smcain@umich.edu.
  • Vitali RV; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. vitalir@umich.edu.
  • Davidson SP; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. stevepd@umich.edu.
  • Stirling LA; Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Boston, MA 02139, USA. leia@mit.edu.
  • Perkins NC; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. ncp@umich.edu.
Sensors (Basel) ; 17(11)2017 Nov 17.
Article em En | MEDLINE | ID: mdl-29149063
ABSTRACT
Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fisiologia / Corrida / Atletas / Dispositivos Eletrônicos Vestíveis Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fisiologia / Corrida / Atletas / Dispositivos Eletrônicos Vestíveis Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article