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F65 Mobile sensor-based gait analysis provides objective motor assessments in huntington’s disease
  1. Dennis Jensen1,
  2. Heiko Gassner1,
  3. Laura Spital2,
  4. Paula Raulet2,
  5. Anja Kletsch2,
  6. Stefan Bohlen2,
  7. Robin Schubert2,
  8. Lisa M Muratori2,
  9. Björn Eskofier3,
  10. Jochen Klucken1,
  11. Jürgen Winkler1,
  12. Ralf Reilmann2,
  13. Zacharias Kohl1
  1. 1Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University (FAU) Erlangen-Nuernberg, Erlangen, Germany
  2. 2George-Huntington-Institute (GHI) GmbH, Muenster, Germany
  3. 3Machine Learning and Data Analytics Lab (MAD-LAB), FAU Erlangen-Nuernberg, Erlangen, Germany


Background Gait disturbance plays an important role for quality of life in patients with Huntington’s disease (HD). Measuring gait parameters in patients with HD is essential for the objective assessment of motor impairments and potential beneficial effects of future treatments.

Aims A mobile sensor-based gait analysis system was used to objectively assess specific features of gait in HD patients compared to healthy controls. Moreover, these measures were correlated to the clinical scores UDHRS Total Motor Score (TMS) and Total Functional Capacity (TFC).

Methods 50 HD patients at two German sites were included in the study and received standardized clinical assessments during their annual ENROLL-HD visit. In addition, HD patients and a cohort of age- and gender matched healthy controls performed defined gait tests consisting of a 4 × 10 m walk, the 2-Minute-Walk-Test, and the Timed Up and Go Test (TUG). The mobile gait analysis system utilized inertial sensors attached to both shoes. Spatio-temporal gait parameters were calculated by machine learning algorithms.

Results Specific gait parameters such as stride length and gait velocity were severely reduced, stride and stance time were significantly increased in patients with HD compared to healthy controls. Parameters describing gait variability were significantly altered in HD subjects and showed strong correlations to TMS and TFC. The objective gait measurements reflected disease stage according to TFC. In contrast, correlations of functional measures (e.g. TUG) were notably weaker.

Conclusions Mobile gait analysis objectively supports the identification of specific features of motor impairment in HD for future clinical trials.

  • gait
  • gait analysis
  • motor assessment

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