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ML Helps Spot Gait Problems in Individuals With Multiple Sclerosis


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Comparing the gait of a healthy adult (left) to that of a person with Multiple Sclerosis

Researchers are integrating gait data and machine learning to advance the tools used to monitor and predict the progression of Multiple Sclerosis.

Credit: University of Illinois at Urbana Champaign News Bureau

Researchers at the University of Illinois Urbana Champaign have developed a method that uses machine learning to help clinicians determine whether gait issues in adults over 50 with multiple sclerosis (MS) are associated with disease progression or aging.

The researchers collected gait data from 40 adults, half with MS and half without, using an instrumented treadmill. They viewed each participant's characteristic spatial, temporal, and kinetic features in their strides to assess variations in gait.

The researchers were able to identify differences in gait patterns between participants by observing changes in various gait features.

Researcher Richard Sowers said, "In this study, we are trying to extract conclusions from datasets that include many measurements of each individual, but a small number of individuals. The results of this study make significant headway in the area of clinical machine learning-based disease-prediction strategies."

From University of Illinois at Urbana Champaign News Bureau
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


 

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