Literature DB >> 27889189

Reliability of gait analysis using wearable sensors in patients with knee osteoarthritis.

Dylan Kobsar1, Sean T Osis2, Angkoon Phinyomark2, Jeffrey E Boyd3, Reed Ferber4.   

Abstract

The aim of this study was to determine the test-retest reliability of linear acceleration waveforms collected at the low back, thigh, shank, and foot during walking, in a cohort of knee osteoarthritis patients, by applying two separate sensor attitude correction methods (static attitude correction and dynamic attitude correction). Linear acceleration data were collected on the subjects׳ most affected limb during treadmill walking on two separate days. Results reveal all attitude corrected acceleration waveforms displayed high repeatability, with coefficient of multiple determination values ranging from 0.82 to 0.99. Overall, mediolateral accelerations and the thigh sensor demonstrated the lowest reliabilities, but interaction effects revealed only mediolateral accelerations at the thigh and foot sensors were different than other axes and sensor locations. Both attitude correction methods led to improved reliability of linear acceleration waveforms. These findings suggest that while all sensor locations and axes display acceptable reliability in a clinical knee osteoarthritis population, the back and shank locations, and the vertical and anteroposterior acceleration directions, are most reliable.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Accelerometer; Biomechanics; Gait; Reliability; Wearable sensors

Mesh:

Year:  2016        PMID: 27889189     DOI: 10.1016/j.jbiomech.2016.11.047

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  11 in total

1.  Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed.

Authors:  Hannah L Dimmick; Cody R van Rassel; Martin J MacInnis; Reed Ferber
Journal:  Sensors (Basel)       Date:  2022-05-29       Impact factor: 3.847

2.  Quantifying varus thrust in knee osteoarthritis using wearable inertial sensors: A proof of concept.

Authors:  Kerry E Costello; Samantha Eigenbrot; Alex Geronimo; Ali Guermazi; David T Felson; Jim Richards; Deepak Kumar
Journal:  Clin Biomech (Bristol, Avon)       Date:  2020-11-11       Impact factor: 2.063

3.  Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters.

Authors:  Felix Kluge; Heiko Gaßner; Julius Hannink; Cristian Pasluosta; Jochen Klucken; Björn M Eskofier
Journal:  Sensors (Basel)       Date:  2017-06-28       Impact factor: 3.576

4.  Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach.

Authors:  Dylan Kobsar; Reed Ferber
Journal:  Sensors (Basel)       Date:  2018-08-27       Impact factor: 3.576

Review 5.  Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis-A Scoping Review.

Authors:  Dylan Kobsar; Zaryan Masood; Heba Khan; Noha Khalil; Marium Yossri Kiwan; Sarah Ridd; Matthew Tobis
Journal:  Sensors (Basel)       Date:  2020-12-13       Impact factor: 3.576

Review 6.  Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review.

Authors:  Mariano Bernaldo de Quirós; E H Douma; Inge van den Akker-Scheek; Claudine J C Lamoth; Natasha M Maurits
Journal:  Sensors (Basel)       Date:  2022-01-28       Impact factor: 3.576

7.  Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis.

Authors:  Kentaro Homan; Keizo Yamamoto; Ken Kadoya; Naoki Ishida; Norimasa Iwasaki
Journal:  BMC Sports Sci Med Rehabil       Date:  2022-04-17

8.  Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis.

Authors:  Dylan Kobsar; Sean T Osis; Jeffrey E Boyd; Blayne A Hettinga; Reed Ferber
Journal:  J Neuroeng Rehabil       Date:  2017-09-12       Impact factor: 4.262

9.  Reliability of 3D Lower Extremity Movement Analysis by Means of Inertial Sensor Technology during Transitional Tasks.

Authors:  Rob van der Straaten; Annick Timmermans; Amber K B D Bruijnes; Benedicte Vanwanseele; Ilse Jonkers; Liesbet De Baets
Journal:  Sensors (Basel)       Date:  2018-08-11       Impact factor: 3.576

Review 10.  The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature.

Authors:  Ashley Cha Yin Lim; Pragadesh Natarajan; R Dineth Fonseka; Monish Maharaj; Ralph J Mobbs
Journal:  Digit Health       Date:  2022-01-27
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