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UID:0-5403@eng.ufl.edu
DTSTART;TZID=America/New_York:20221017T150000
DTEND;TZID=America/New_York:20221017T160000
DTSTAMP:20251201T181910Z
URL:https://www.eng.ufl.edu/news-events/events/bme-seminar-joint-loading-p
 atterns-and-knee-osteoarthritis-progression/
SUMMARY:BME Seminar:  Joint Loading Patterns and Knee Osteoarthritis Progre
 ssion
DESCRIPTION:Dr. Kerry Costello is an assistant professor in the department 
 of Mechanical and Aerospace Engineering. Prior to joining the University o
 f Florida\, she completed a postdoctoral fellowship at Boston University i
 n the Department of Physical Therapy &amp\; Athletic Training and the Sect
 ion of Rheumatology\; a doctoral degree in biomedical engineering at Dalho
 usie University in Halifax\, Nova Scotia\, Canada\; a master’s degree in
  biomedical engineering at Virginia Tech\; and her undergraduate degree in
  biomedical and mechanical engineering at Duke University. Dr. Costello al
 so spent a year doing research at a private orthopedic sports medicine res
 earch institute in Colorado and a year completing a Fulbright scholarship 
 at Vrije Universiteit Amsterdam\, the Netherlands. Her research utilizes m
 otion capture data\, wearable sensor data\, and signal analysis and machin
 e learning tools to understand how time-varying\, multidimensional joint l
 oading patterns during human movement contribute to disease progression in
  knee osteoarthritis. She also created\, hosts\, and produces the Osteoart
 hritis Research Society International’s ‘Hey OA’ podcast.\nKerry Cos
 tello\, Ph.D.\, Assistant Professor\, Department of Mechanical &amp\; Aero
 space Engineering\, University of Florida\nAbstract:\nMechanical loading o
 n the knee joint during human movement is one of the only modifiable risk 
 factors for knee osteoarthritis\, a painful disease affecting over 350 mil
 lion people worldwide. Gait analysis studies have identified key features 
 of joint loading during walking that are associated with disease progressi
 on\, in particular the knee adduction moment magnitude. However\, the tiss
 ues of the joint respond not only to the magnitude\, but also to the time-
 varying\, multi-dimensional patterns of joint loading exposure. Better cha
 racterization of these loading patterns and their role in the disease proc
 ess could lead to improved conservative management for knee osteoarthritis
 \, such as patient-specific recommendations for biomechanical intervention
 s\, physical therapy\, and/or physical activity type\, intensity\, and fre
 quency. The technologies used to capture gait data in a laboratory setting
  and physical activity data in a real-world setting (e.g.\, accelerometers
 ) provide a wealth of detailed information about how people move and the a
 ssociated loading patterns during movement. However\, the complex interact
 ions among gait\, physical activity\, and patient-specific factors (e.g.\,
  age\, sex\, disease severity) and the time-varying\, multidimensional nat
 ure of these signals make traditional analyses challenging. This talk pres
 ents research exploring data science and machine learning approaches to an
 alyze these complex loading patterns and provide insight into the role of 
 human movement in knee osteoarthritis progression.
CATEGORIES:Seminars
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