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Automated gait event detection for a variety of locomotion tasks using a novel gyroscope-based algorithm

Authors :
Frieder C. Krafft
Thorsten Stein
Stefan Sell
Cagla Fadillioglu
Steffen Ringhof
Bernd J. Stetter
Source :
Gait & posture, 81, 102–108
Publication Year :
2019

Abstract

Background The robust identification of initial contact (IC) and toe-off (TO) events is a substantial task in mobile sensor-based gait analysis. Shank attached gyroscopes in combination with suitable algorithms for data processing can robustly and accurately complet this task for gait event detection. However, little research has considered gait detection algorithms that are applicable to different locomotion tasks. Research question Does a gait event detection algorithm for various locomotion tasks yield comparable estimation accuracies as approaches for individual tasks? Methods Thirteen males, equipped with a gyroscope attached to the right shank, volunteered to perform nine different locomotion tasks consisting of linear motions and motions with a change of direction. A rule-based algorithm for IC and TO events was developed based on the shank sagittal plane angular velocity. The algorithm was evaluated against events determined by vertical ground reaction forces, and absolute mean error, relative absolute mean error and Bland–Altman analysis assessed its accuracy. Results The average absolute mean error and relative absolute mean error were 11 ± 3 ms and 3.07 ± 1.33%, respectively, for IC and 29 ± 11 ms and 7.27 ± 2.92%, respectively, for TO. Alterations of the walking movement, such as turns and types of running, slightly reduced the accuracy of IC and TO detection. In comparison to previous methods, increased or comparable accuracies for both IC and TO detection are shown. Significance These results support the feasibility of a shank attached gyroscope for gait event detection in various locomotion tasks. A comprehensive algorithm that is applicable to different locomotion tasks has the advantage that not every movement must be modeled individually. Ultimately, this enlarges the use of mobile sensor-based gait analysis.

Details

ISSN :
18792219 and 09666362
Volume :
81
Database :
OpenAIRE
Journal :
Gaitposture
Accession number :
edsair.doi.dedup.....090aee03431d2faf609227072a5fa3b0