Classification of Neurological Gait Disorders Using Multi-task Feature Learning

Ioannis Papavasileiou, Wenlong Zhang, Xin Wang, Jinbo Bi, Li Zhang, Song Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

As our population ages, neurological impairments and degeneration of the musculoskeletal system yield gait abnormalities, which can significantly reduce quality of life. Gait rehabilitative therapy has been widely adopted to help patients maximize community participation and living independence. To further improve the precision and efficiency of rehabilitative therapy, more objective methods need to be developed based on sensory data. In this paper, an algorithmic framework is proposed to provide classification of gait disorders caused by two common neurological diseases, stroke and Parkinson's Disease (PD), from ground contact force (GCF) data. An advanced machine learning method, multi-task feature learning (MTFL), is used to jointly train classification models of a subject's gait in three classes, post-stroke, PD and healthy gait. Gait parameters related to mobility, balance, strength and rhythm are used as features for the classification. Out of all the features used, the MTFL models capture the more important ones per disease, which will help provide better objective assessment and therapy progress tracking. To evaluate the proposed methodology we use data from a human participant study, which includes five PD patients, three post-stroke patients, and three healthy subjects. Despite the diversity of abnormalities, the evaluation shows that the proposed approach can successfully distinguish post-stroke and PD gait from healthy gait, as well as post-stroke from PD gait, with Area Under the Curve (AUC) score of at least 0.96. Moreover, the methodology helps select important gait features to better understand the key characteristics that distinguish abnormal gaits and design personalized treatment.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 2nd International Conference on Connected Health
Subtitle of host publicationApplications, Systems and Engineering Technologies, CHASE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages195-204
Number of pages10
ISBN (Electronic)9781509047215
DOIs
StatePublished - Aug 14 2017
Event2nd IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017 - Philadelphia, United States
Duration: Jul 17 2017Jul 19 2017

Publication series

NameProceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017

Other

Other2nd IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017
CountryUnited States
CityPhiladelphia
Period7/17/177/19/17

Keywords

  • gait analysis
  • gait disorder diagnosis
  • gait pattern recognition
  • multi-task learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Health(social science)
  • Communication
  • Computer Science Applications
  • Software
  • Biomedical Engineering
  • Health Informatics

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