Turn Detection and Characterization with Inertial Sensors

Sean Pearson, Martina Mancini, Mahmoud El-Gohary, James McNames, Fay Horak


Turn detection and characterization in the home is important for continuous assessment of gait and balance in people with movement disability. Turning often results in falling in individuals with movement disorders. Researchers and clinicians would benefit from a system that identifies and characterizes their daily mobility behavior to predict their risk of falling, benefits or side effects of treatment, and progression of disease. The goal of this study is to develop an algorithm that is capable of reliably detecting turns during gait with the goal of applying it over long periods outside a lab environment. Performance of the algorithm is validated against an optical marker system and video analysis of a subset of the participants.


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Paper Citation

in Harvard Style

Pearson S., Mancini M., El-Gohary M., McNames J. and Horak F. (2013). Turn Detection and Characterization with Inertial Sensors . In Proceedings of the International Congress on Sports Science Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-8565-79-2, pages 19-22. DOI: 10.5220/0004647000190022

in Bibtex Style

author={Sean Pearson and Martina Mancini and Mahmoud El-Gohary and James McNames and Fay Horak},
title={Turn Detection and Characterization with Inertial Sensors},
booktitle={Proceedings of the International Congress on Sports Science Research and Technology Support - Volume 1: icSPORTS,},

in EndNote Style

JO - Proceedings of the International Congress on Sports Science Research and Technology Support - Volume 1: icSPORTS,
TI - Turn Detection and Characterization with Inertial Sensors
SN - 978-989-8565-79-2
AU - Pearson S.
AU - Mancini M.
AU - El-Gohary M.
AU - McNames J.
AU - Horak F.
PY - 2013
SP - 19
EP - 22
DO - 10.5220/0004647000190022