Literature DB >> 25570616

Quantitative assessment of multiple sclerosis using inertial sensors and the TUG test.

Barry R Greene, Michael Healy, Stephanie Rutledge, Brian Caulfield, Niall Tubridy.   

Abstract

Multiple sclerosis (MS) is a progressive neurological disorder affecting between 2 and 2.5 million people globally. Tests of mobility form part of clinical assessments of MS. Quantitative assessment of mobility using inertial sensors has the potential to provide objective, longitudinal monitoring of disease progression in patients with MS. The mobility of 21 patients (aged 25-59 years, 8 M, 13 F), diagnosed with relapsing-remitting MS was assessed using the Timed up and Go (TUG) test, while patients wore shank-mounted inertial sensors. This exploratory, cross-sectional study aimed to examine the reliability of quantitative measures derived from inertial sensors during the TUG test, in patients with MS. Furthermore, we aimed to determine if disease status (as measured by the Multiple Sclerosis Impact Scale (MSIS-29) and the Expanded Disability Status Score (EDSS)) can be predicted by assessment using a TUG test and inertial sensors. Reliability analysis showed that 32 of 52 inertial sensors parameters obtained during the TUG showed excellent intrasession reliability, while 11 of 52 showed moderate reliability. Using the inertial sensors parameters, regression models of the EDSS and MSIS-29 scales were derived using the elastic net procedure. Using cross validation, an elastic net regularized regression model of MSIS yielded a mean square error (MSE) of 334.6 with 25 degrees of freedom (DoF). Similarly, an elastic net regularized regression model of EDSS yielded a cross-validated MSE of 1.5 with 6 DoF. Results suggest that inertial sensor parameters derived from MS patients while completing the TUG test are reliable and may have utility in assessing disease state as measured using EDSS and MSIS.

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Year:  2014        PMID: 25570616     DOI: 10.1109/EMBC.2014.6944248

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  7 in total

1.  Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis.

Authors:  Lindsey J Tulipani; Brett Meyer; Dale Larie; Andrew J Solomon; Ryan S McGinnis
Journal:  Gait Posture       Date:  2020-06-20       Impact factor: 2.840

2.  Instrumented balance and walking assessments in persons with multiple sclerosis show strong test-retest reliability.

Authors:  Jordan J Craig; Adam P Bruetsch; Sharon G Lynch; Fay B Horak; Jessie M Huisinga
Journal:  J Neuroeng Rehabil       Date:  2017-05-22       Impact factor: 4.262

3.  Turning is an important marker of balance confidence and walking limitation in persons with multiple sclerosis.

Authors:  Gautam Adusumilli; Samantha Lancia; Victoria A Levasseur; Vaishak Amblee; Megan Orchard; Joanne M Wagner; Robert T Naismith
Journal:  PLoS One       Date:  2018-06-07       Impact factor: 3.240

4.  Quantifying turning behavior and gait in Parkinson's disease using mobile technology.

Authors:  Mandy Miller Koop; Sarah J Ozinga; Anson B Rosenfeldt; Jay L Alberts
Journal:  IBRO Rep       Date:  2018-06-21

5.  Detection of postural sway abnormalities by wireless inertial sensors in minimally disabled patients with multiple sclerosis: a case-control study.

Authors:  Andrew J Solomon; Jesse V Jacobs; Karen V Lomond; Sharon M Henry
Journal:  J Neuroeng Rehabil       Date:  2015-09-01       Impact factor: 4.262

6.  Thigh-Derived Inertial Sensor Metrics to Assess the Sit-to-Stand and Stand-to-Sit Transitions in the Timed Up and Go (TUG) Task for Quantifying Mobility Impairment in Multiple Sclerosis.

Authors:  Harry J Witchel; Cäcilia Oberndorfer; Robert Needham; Aoife Healy; Carina E I Westling; Joseph H Guppy; Jake Bush; Jens Barth; Chantal Herberz; Daniel Roggen; Björn M Eskofier; Waqar Rashid; Nachiappan Chockalingam; Jochen Klucken
Journal:  Front Neurol       Date:  2018-09-14       Impact factor: 4.003

7.  Short Bouts of Gait Data and Body-Worn Inertial Sensors Can Provide Reliable Measures of Spatiotemporal Gait Parameters from Bilateral Gait Data for Persons with Multiple Sclerosis.

Authors:  Lilian Genaro Motti Ader; Barry R Greene; Killian McManus; Niall Tubridy; Brian Caulfield
Journal:  Biosensors (Basel)       Date:  2020-09-20
  7 in total

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