Feature learning for detection and prediction of freezing of gait in Parkinson's disease

Sinziana Mazilu, Alberto Calatroni, Eran Gazit, Daniel Roggen, Jeffrey M. Hausdorff, Gerhard Tröster

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impact the patient's quality of life. Wearable systems that detect FoG have been developed to help patients resume walking by means of auditory cueing. However, current methods for automated detection are not yet ideal. In this paper, we first compare feature learning approaches based on time-domain and statistical features to unsupervised ones based on principal components analysis. The latter systematically outperforms the former and also the standard in the field - Freezing Index by up to 8.1% in terms of F1-measure for FoG detection. We go a step further by analyzing FoG prediction, i.e., identification of patterns (pre-FoG) occurring before FoG episodes, based only on motion data. Until now this was only attempted using electroencephalography. With respect to the three-class problem (FoG vs. pre-FoG vs. normal locomotion), we show that FoG prediction performance is highly patient-dependent, reaching an F1-measure of 56% in the pre-FoG class for patients who exhibit enough gait degradation before FoG.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 9th International Conference, MLDM 2013, Proceedings
Pages144-158
Number of pages15
DOIs
StatePublished - 2013
Externally publishedYes
Event9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013 - New York, NY, United States
Duration: 19 Jul 201325 Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7988 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013
Country/TerritoryUnited States
CityNew York, NY
Period19/07/1325/07/13

Funding

FundersFunder number
Seventh Framework Programme288516

    Keywords

    • Freezing of Gait
    • Parkinson's disease
    • Unsupervised feature learning

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