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TZID:America/Phoenix
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DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203101Z
LOCATION:Grand Ballroom
DTSTART;TZID=America/Phoenix:20240910T133000
DTEND;TZID=America/Phoenix:20240910T135000
UID:HFESAM_ASPIRE - Presented by HFES_sess141_LECT104@linklings.com
SUMMARY:Classifying Restricted Knee Flexion and Affected Sides Using Plant
 ar Pressure Data During Walking: A Preliminary Study
DESCRIPTION:Lecture\n\nSeobin Choi (Oregon State University) and Gwanseob 
 Shin (Ulsan National Institute of Science & Technology)\n\nDiminished knee
  flexion during walking, commonly observed in neuromuscular and knee-relat
 ed diseases, compromises gait stability and increases the risk of falls. T
 his study developed an accessible, non-invasive algorithm using pressure p
 latform data to predict restricted knee flexion and affected sides. Sixty 
 healthy young adults performed walking trials on a 10m path with a 1.5m pr
 essure platform, under both normal and restricted conditions. Their left a
 nd right knees were constrained to 20 and 40 degrees using a knee brace in
  a randomized order. We employed machine learning techniques, including fe
 ature selection, stratified 10-fold cross-validation, and hyperparameter o
 ptimization, on datasets split into 80% training and 20% testing sets. The
  optimized Support Vector Machine distinguished between normal and restric
 ted conditions and identified the affected side with 95% accuracy. This pr
 eliminary study provides valuable insights and could serve as a practical 
 tool for enhancing mobility assessments in patients with neuromuscular and
  knee-related conditions.\n\nTrack: Health Care\n\nSession Chair: Kermit D
 avis (University of Cincinnati)
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