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

Annu Int Conf IEEE Eng Med Biol Soc. 2014:2014:2977-80. doi: 10.1109/EMBC.2014.6944248.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Disability Evaluation
  • Female
  • Humans
  • Male
  • Middle Aged
  • Multiple Sclerosis / diagnosis*
  • Neurophysiology / instrumentation*
  • Neurophysiology / methods*
  • Regression Analysis
  • Reproducibility of Results
  • Signal Processing, Computer-Assisted