Reference : What are the optimal walking tests to assess disability progression?
Scientific congresses and symposiums : Paper published in a journal
Engineering, computing & technology : Electrical & electronics engineering
What are the optimal walking tests to assess disability progression?
Pierard, Sébastien mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Van Droogenbroeck, Marc mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Multiple Sclerosis Journal
SAGE Publications
Yes (verified by ORBi)
United Kingdom
European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS)
14-17 September 2016
[en] GAIMS ; Gait analysis ; Multiple Sclerosis
[en] Background. Therapy success is assumed when there is no evidence of disease activity. Clues to show it include an MRI, the relapses history, questionnaires, and clinical measures to assess the disability progression. Especially gait analysis plays a major role as gait impairment is considered by patients as the most disabling symptom. Too often only the walking speed is measured. New technologies (eg GAIMS, see ECTRIMS 2012-15) measure many spatiotemporal gait parameters, even during long tests (\eg 6min, 500m), without equipping patients with markers or sensors. Moreover, various tests can be done, depending on the length and type of walk (comfortable pace --C--, as fast as possible --F--, tandem gait --T--). Objective. Determine if there is an advantage to perform various walking tests, and which test or combination of tests brings the higher amount of information about the patient state in a reasonable amount of acquisition time. Methods. The system GAIMS provided 434 recordings of the gait parameters of healthy people and 60 recordings of MS patients with EDSS<= 4. They performed 12 tests (25ft C+F+T each twice, 20m C+F+T, 100m C+F, 500m F). To assess the ability of these clinical outcome measures to detect disability progression, we evaluate the possibility of differentiating the persons below a given EDSS threshold (0.25) from those above it based only on the measured gait parameters. For individual tests, we use the classifier of Azrour (ESANN 2014). All subsets of the tests are also considered, by combining the individual classifiers and determining automatically the optimal relative importance of the tests with the linear support vector machine (SVM) technique. The ability to detect the disability progression is quantified by the performance (area under the ROC curve --AUC-- and the maximum achievable balanced accuracy --MBA--) of the corresponding classifiers. Results. The best test alone is the 500m F (note that the walking speed measured during it is the gait parameter best correlated with the EDSS). Combining several tests leads to a better performance. A performance (MBA=95.7%, AUC=0.983) close to the best achievable one can be obtained with 6 tests only (25ft C twice, 25tf F twice, 20m C, 20m T). Conclusions. The clinical gait analysis can help to detect disability progression. While considering different types of walking tests improves the ability of taking decisions, we showed that performing 6 tests for a total of 70.48m suffices.
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