TY - JOUR
T1 - Estimating stair running performance using inertial sensors
AU - Ojeda, Lauro V.
AU - Zaferiou, Antonia M.
AU - Cain, Stephen M.
AU - Vitali, Rachel V.
AU - Davidson, Steven P.
AU - Stirling, Leia A.
AU - Perkins, Noel C.
N1 - Publisher Copyright:
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2017/11/17
Y1 - 2017/11/17
N2 - 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.
AB - 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.
KW - Human performance
KW - Inertial measurement units
KW - Motion tracking
KW - Stair running
KW - Wearable sensors
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U2 - 10.3390/s17112647
DO - 10.3390/s17112647
M3 - Article
C2 - 29149063
AN - SCOPUS:85034854114
SN - 1424-8220
VL - 17
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 11
M1 - 2647
ER -