PhD Defense by Daniel Whittingslow

Event Details
  • Date/Time:
    • Tuesday October 22, 2019 - Wednesday October 23, 2019
      11:00 am - 12:59 pm
  • Location: Room 114, CODA Building, Tech Square, Georgia Tech
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Summary Sentence: Anatomy of a Joint Sound – Using Joint Acoustic Emissions to Diagnose and Grade Musculoskeletal Disease and Injury

Full Summary: No summary paragraph submitted.

Daniel Whittingslow

Biomedical Engineering PhD Thesis Defense


Date: Tuesday, October 22nd, 2019

Time: 11:00am - 1:00pm

Location: Room 114, CODA Building, Tech Square, Georgia Tech

Address: 756 W Peachtree St NW, Atlanta, GA 30308



Omer T. Inan, PhD


Committee Members:

Rob Butera, PhD

Young-Hui Chang, PhD

Shelly Abramowicz, DMD, MPH, FACS

Sampath Prahalad, MD


Title: Anatomy of a Joint Sound – Using Joint Acoustic Emissions to Diagnose and Grade Musculoskeletal Disease and Injury



Knee injuries and chronic disorders, such as arthritis, affect millions of Americans. Currently, diagnosis of these conditions relies primarily on imaging studies and physical examination by a health care professional. After diagnosis, there are few quantitative technologies available to provide feedback to patients regarding rehabilitation or efficacy of treatments. To address this need, I have developed a device capable of recording and analyzing a joint’s acoustic emissions (AEs). In this work, I developed a human cadaver model of acute knee injury and found a consistent and repeatable variation of the observed AE patterns. This study helped us better understand the underlying anatomical contributors to these AE patterns. I then translated this technology into a clinical study and performed cross-sectional and longitudinal recordings of two groups of patients: a pediatric cohort with juvenile idiopathic arthritis (JIA), and an adult cohort with acute, traumatic injuries. With those patients recorded, I computed features based on their low-level acoustic emission signals. Machine learning algorithms fused those feature sets into an easily interpretable joint health metric for use in clinical diagnosis and decision making.


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Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Oct 7, 2019 - 1:39pm
  • Last Updated: Oct 7, 2019 - 1:39pm