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http://dx.doi.org/10.4218/etrij.2018-0639

Gait event detection algorithm based on smart insoles  

Kim, JeongKyun (School of Computer Software, ICT, University of Science and Technology)
Bae, Myung-Nam (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute)
Lee, Kang Bok (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute)
Hong, Sang Gi (School of Computer Software, ICT, University of Science and Technology)
Publication Information
ETRI Journal / v.42, no.1, 2020 , pp. 46-53 More about this Journal
Abstract
Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel-strike and toe-off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel-strike and toe-off detection algorithm using a smart insole with inertial measurement units. This method detects heel-strike and toe-off events through a time-frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel-strike and toe-off detection algorithm yielded the largest error of 0.03 seconds for running toe-off events, and an average of 0-0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single-support time, and double-support time can all be estimated using the proposed heel-strike and toe-off detection algorithm.
Keywords
gait analysis; heel-strike detection; smart insole; time-frequency analysis; toe-off detection;
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