Validating Joint Acoustic Emissions Models as a Generalizable Predictor of Joint Health

Kristine L. Richardson, Christopher J. Nichols, Rachel G. Stegeman, Daniel P. Zachs, Adam Tuma, J. Alex Heller, Thomas Schnitzer, Erik J. Peterson, Hubert H. Lim, Mozziyar Etemadi, David Ewart, Omer T. Inan

Research output: Contribution to journalArticlepeer-review

Abstract

Joint acoustic emissions (JAEs) have been used as a noninvasive sensing modality of joint health for different conditions such as acute injuries, osteoarthritis (OA), and rheumatoid arthritis (RA). Recent hardware improvements for sensing JAEs have made at-home sensing to supplement clinical visits a possibility. To complement these advances, models must be improved for JAEs to function as generalizable predictors of joint health. Addressing this need, this work investigates the effects of recording setup, location-specific factors, and participant population on previously validated JAE models. The effect of recording setup is first investigated by testing a model developed previously for a wearable brace to predict erythrocyte sedimentation rate (ESR) in participants with RA on benchtop data, resulting in an area under the receiver-operating characteristic curve (AUC), sensitivity, and specificity of 0.79, 0.73, and 0.81, respectively. Investigating the effects of participant population type and location-specific factors, a feature-based model and a convolutional neural network (CNN) were both trained with healthy and RA data to predict ESR level, and then tested on a new dataset containing healthy, preradiographic OA (Pre-OA), and OA data. The feature-based model had an AUC of 0.69 and 0.94, a sensitivity of 0.38 and 0.80, and a sensitivity of 1, while the CNN had an AUC of 0.85 and 0.99, a sensitivity of 0.50 and 1, and a specificity of 0.90 for detecting Pre-OA and OA, respectively. The ability to generalize models across setup, location, and participant population provides a foundation for using JAEs as a measure of joint health.

Original languageEnglish (US)
Pages (from-to)17219-17230
Number of pages12
JournalIEEE Sensors Journal
Volume24
Issue number10
DOIs
StatePublished - May 15 2024

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.

Keywords

  • Arthritis
  • joint acoustic emissions (JAEs)
  • machine learning
  • wearable sensing

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