Ph.D. Dissertation Defense - Hyeon Ki Jeong

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Event Details
  • Date/Time:
    • Wednesday July 7, 2021
      8:30 am - 11:30 am
  • Location: https://bluejeans.com/897161852/9921
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Summaries

Summary Sentence: Quantifying the Effects of Knee Joint Biomechanics on Acoustical Emissions

Full Summary: No summary paragraph submitted.

TitleQuantifying the Effects of Knee Joint Biomechanics on Acoustical Emissions

Committee:

Dr. Omer Inan, ECE, Chair , Advisor

Dr. Aaron Young, ME

Dr. Farrokh Ayazi, ECE

Dr. Mindy Millard-Stafford, Biological Sciences

Dr. Thomas Ploetz, IC

Abstract: The knee is one of the most injured body parts, causing 18 million patients to be seen in clinics every year. Because the knee is a weight bearing joint, it is prone to pathologies such as osteoarthritis and ligamentous injuries. Existing technologies for monitoring knee health can provide accurate assessment and diagnosis for acute injuries. However, they are mainly confined to clinical or laboratory settings only, time-consuming, expensive, and not well-suited for longitudinal monitoring. Developing a novel technology for joint health assessment beyond the clinic can further provide insights on the rehabilitation process and quantitative usage of the knee joint. To better understand the underlying properties and fundamentals of joint sounds, this research will investigate the relationship between the changes in the knee joint structure (i.e. structural damage and joint contact force) and the JAEs while developing novel techniques for analyzing these sounds. We envision that the possibility of quantifying joint structure and joint load usage from these acoustic sensors would advance the potential of JAE as the next biomarker of joint health that can be captured with wearable technology. First, we developed a novel processing technique for JAEs that quantify on the structural change of the knee from injured athletes and human lower-limb cadaver models. Second, we quantified whether JAEs can detect the increase in the mechanical stress on the knee joint using an unsupervised graph mining algorithm. Lastly, we quantified the directional bias of the load distribution between medial and lateral compartment using JAEs. Understanding and monitoring the quantitative usage of knee loads in daily activities can broaden the implications for longitudinal joint health monitoring.

Additional Information

In Campus Calendar
No
Groups

ECE Ph.D. Dissertation Defenses

Invited Audience
Public
Categories
Other/Miscellaneous
Keywords
Phd Defense, graduate students
Status
  • Created By: Daniela Staiculescu
  • Workflow Status: Published
  • Created On: Jul 2, 2021 - 3:26pm
  • Last Updated: Jul 2, 2021 - 3:26pm