Predicting Multiple Sclerosis from Gait Dynamics Using an Instrumented Treadmill – A Machine Learning Approach: A Machine Learning Approach

Rachneet Kaur, Zizhang Chen, Robert Motl, Manuel Enrique Hernandez, Richard Sowers

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: Multiple Sclerosis (MS) is a neurological condition which widely affects people 50-60 years of age. While clinical presentations of MS are highly heterogeneous, mobility limitations are one of the most frequent symptoms. This study examines a machine learning (ML) framework for identifying MS through spatiotemporal and kinetic gait features. Methods: In this study, gait data during self-paced walking on an instrumented treadmill from 20 persons with MS and 20 age, weight, height, and gender-matched healthy older adults (HOA) were obtained. We explored two strategies to normalize data and minimize dependence on subject demographics; size-normalization (standard body size-based normalization) and regress-normalization (regression-based normalization using scaling factors derived by regressing gait features on multiple subject demographics); and proposed an ML based methodology to classify individual strides of older persons with MS (PwMS) from healthy controls. We generalized both across different walking tasks and subjects. Results: We observed that regress-normalization improved the accuracy of identifying pathological gait using ML when compared to size-normalization. When generalizing from comfortable walking to walking while talking, gradient boosting machine achieved the optimal subject classification accuracy and AUC of 94.3 and 1.0, respectively and for subject generalization, a multilayer perceptron resulted in the best accuracy and AUC of 80% and 0.86, respectively, both with regression-normalized data. Conclusion: The integration of gait data and ML may provide a viable patient-centric approach to aid clinicians in monitoring MS. Significance: The results of this study have future implications for the way regression normalized gait features may be clinically used to design ML-based disease prediction strategies and monitor disease progression in PwMS.

Original languageEnglish (US)
Article number9311191
Pages (from-to)2666-2677
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume68
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Conditional entropy
  • Diseases
  • Gait
  • Kinetic theory
  • Legged locomotion
  • Machine learning
  • Monitoring
  • Multiple sclerosis
  • Progression space
  • Pulse width modulation
  • Spatiotemporal phenomena
  • Task analysis
  • conditional entropy
  • machine learning
  • progression space
  • gait

ASJC Scopus subject areas

  • Biomedical Engineering

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