Summary:
«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 are integrating gait data and machine learning to advance the tools used to monitor and predict disease progression.
A new study of this approach led by University of Illinois Urbana Champaign graduate student Rachneet Kaur, kinesiology and community health professor Manuel Hernandez and industrial and enterprise engineering and mathematics professor Richard Sowers is published in the journal Institute of Electrical and Electronics Engineers Transactions on Biomedical Engineering.»
Article written by UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN, NEWS BUREAU
29|03|2021
Source:
EurekAlert!
https://www.eurekalert.org/pub_releases/2021-03/uoia-mlh032621.php