Monitoring the progression of multiple sclerosis-related gait issues can be challenging in adults over 50 years old, requiring a clinician to differentiate between problems related to MS and other age-related issues. To address this problem, researchers integrated gait data and machine learning to advance the tools used to monitor and predict disease progression.

Multiple sclerosis can present itself in many ways in the approximately two million people that it affects globally, and walking problems are a common symptom. About half of the patients need walking assistance within 15 years of onset. The researchers sought to determine the interactions between aging and concurrent MS disease-related changes and whether they could differentiate between the two in older adults with MS. Machine-learning techniques work particularly well at spotting complex hidden changes in performance.

Using an instrumented treadmill, the team collected gait data — normalized for body size and demographics — from 20 adults with MS and 20 age-, weight-, height-, and gender-matched older adults without MS. The participants walked at a comfortable pace for up to 75 seconds while specialized software captured gait events, corresponding ground reaction forces, and center-of-pressure positions during each walk. The team extracted each participant’s characteristic spatial, temporal, and kinetic features in their strides to examine variations in gait during each trial.

Changes in various gait features, including a data feature called the butterfly diagram, helped the team detect differences in gait patterns between participants. The diagram gains its name from the butterfly-shaped curve created from the repeated center-of-pressure trajectory for multiple continuous strides during a subject’s walk and is associated with critical neurological functions.

Biomechanical systems, such as walking, are poorly modeled systems, making it difficult to spot problems in a clinical setting. The new method extracted conclusions from data sets that include many measurements of each individual but a small number of individuals. The results of the work make significant headway in the area of clinical machine learning-based disease-prediction strategies.

For more information, contact Professor Richard Sowers at This email address is being protected from spambots. You need JavaScript enabled to view it.; 217-333-6246.


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This article first appeared in the August, 2021 issue of Tech Briefs Magazine.

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