Improved method of step length estimation based on inverted pendulum model

Qi Zhao, Boxue Zhang, Jingjing Wang, Wenquan Feng, Wenyan Jia and Mingui Sun

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 4, 1550147717702914

Abstract: Step length estimation is an important issue in areas such as gait analysis, sport training, or pedestrian localization. In this article, we estimate the step length of walking using a waist-worn wearable computer named eButton. Motion sensors within this device are used to record body movement from the trunk instead of extremities. Two signal-processing techniques are applied to our algorithm design. The direction cosine matrix transforms vertical acceleration from the device coordinates to the topocentric coordinates. The empirical mode decomposition is used to remove the zero- and first-order skew effects resulting from an integration process. Our experimental results show that our algorithm performs well in step length estimation. The effectiveness of the direction cosine matrix algorithm is improved from 1.69% to 3.56% while the walking speed increased.

Keywords: Step length; inverted pendulum model; wearable computer; empirical mode decomposition; inertia measurement unit sensors (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:4:p:1550147717702914

DOI: 10.1177/1550147717702914

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