Literature DB >> 22877845

Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors.

Benoit Mariani1, Hossein Rouhani, Xavier Crevoisier, Kamiar Aminian.   

Abstract

Time periods composing stance phase of gait can be clinically meaningful parameters to reveal differences between normal and pathological gait. This study aimed, first, to describe a novel method for detecting stance and inner-stance temporal events based on foot-worn inertial sensors; second, to extract and validate relevant metrics from those events; and third, to investigate their suitability as clinical outcome for gait evaluations. 42 subjects including healthy subjects and patients before and after surgical treatments for ankle osteoarthritis performed 50-m walking trials while wearing foot-worn inertial sensors and pressure insoles as a reference system. Several hypotheses were evaluated to detect heel-strike, toe-strike, heel-off, and toe-off based on kinematic features. Detected events were compared with the reference system on 3193 gait cycles and showed good accuracy and precision. Absolute and relative stance periods, namely loading response, foot-flat, and push-off were then estimated, validated, and compared statistically between populations. Besides significant differences observed in stance duration, the analysis revealed differing tendencies with notably a shorter foot-flat in healthy subjects. The result indicated which features in inertial sensors' signals should be preferred for detecting precisely and accurately temporal events against a reference standard. The system is suitable for clinical evaluations and provides temporal analysis of gait beyond the common swing/stance decomposition, through a quantitative estimation of inner-stance phases such as foot-flat.
Copyright © 2012 Elsevier B.V. All rights reserved.

Entities:  

Mesh:

Year:  2012        PMID: 22877845     DOI: 10.1016/j.gaitpost.2012.07.012

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  54 in total

1.  Accelerometry data in health research: challenges and opportunities.

Authors:  Marta Karas; Jiawei Bai; Marcin Strączkiewicz; Jaroslaw Harezlak; Nancy W Glynn; Tamara Harris; Vadim Zipunnikov; Ciprian Crainiceanu; Jacek K Urbanek
Journal:  Stat Biosci       Date:  2019-01-12

2.  Gait Cycle Validation and Segmentation Using Inertial Sensors.

Authors:  G V Prateek; Pietro Mazzoni; Gammon M Earhart; Arye Nehorai
Journal:  IEEE Trans Biomed Eng       Date:  2019-11-25       Impact factor: 4.538

3.  Changes in Parkinsonian gait kinematics with self-generated and externally-generated cues: a comparison of responders and non-responders.

Authors:  Elinor C Harrison; Adam P Horin; Peter S Myers; Kerri S Rawson; Gammon M Earhart
Journal:  Somatosens Mot Res       Date:  2020-01-27       Impact factor: 1.111

4.  Role of body-worn movement monitor technology for balance and gait rehabilitation.

Authors:  Fay Horak; Laurie King; Martina Mancini
Journal:  Phys Ther       Date:  2014-12-11

5.  A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking.

Authors:  Benedikt Fasel; Cyntia Duc; Farzin Dadashi; Flavien Bardyn; Martin Savary; Pierre-André Farine; Kamiar Aminian
Journal:  Med Biol Eng Comput       Date:  2017-02-14       Impact factor: 2.602

6.  Development of a prototype of portable FES rehabilitation system for relearning of gait for hemiplegic subjects.

Authors:  Takashi Watanabe; Shun Endo; Ryusei Morita
Journal:  Healthc Technol Lett       Date:  2016-09-12

7.  Gait event detection using inertial measurement units in people with transfemoral amputation: a comparative study.

Authors:  Emeline Simonetti; Coralie Villa; Joseph Bascou; Giuseppe Vannozzi; Elena Bergamini; Hélène Pillet
Journal:  Med Biol Eng Comput       Date:  2019-12-23       Impact factor: 2.602

Review 8.  Fall Risk Assessment Using Wearable Sensors: A Narrative Review.

Authors:  Rafael N Ferreira; Nuno Ferrete Ribeiro; Cristina P Santos
Journal:  Sensors (Basel)       Date:  2022-01-27       Impact factor: 3.576

9.  Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors.

Authors:  Jeong-Kyun Kim; Myung-Nam Bae; Kang Bok Lee; Sang Gi Hong
Journal:  Sensors (Basel)       Date:  2021-03-04       Impact factor: 3.576

10.  Spatio-temporal gait parameters obtained from foot-worn inertial sensors are reliable in healthy adults in single- and dual-task conditions.

Authors:  J Soulard; J Vaillant; R Balaguier; N Vuillerme
Journal:  Sci Rep       Date:  2021-05-13       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.