An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions

• Published in: Annals of biomedical engineering Vol. 49; no. 9; pp. 2399 - 2411
• Main Authors: Ozmen, Goktug C., Gazi, Asim H., Gharehbaghi, Sevda, Richardson, Kristine L., Safaei, Mohsen, Whittingslow, Daniel C., Prahalad, Sampath, Hunnicutt, Jennifer L., Xerogeanes, John W., Snow, Teresa K., Inan, Omer T.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2021; Springer Nature B.V
• Subjects: Acoustic emission; Acoustic emission testing; Acoustics; Adolescent; Adult; Algorithms; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Classification; Classifiers; Data augmentation; Data collection; Diagnosis; Emission measurements; Experimental data; Feature extraction; Female; Humans; Injuries; Interference; Joints (anatomy); Knee; Knee Joint - physiology; Learning algorithms; Machine Learning; Male; Middle Aged; Original Article; Sensitivity; Signal Processing, Computer-Assisted; Temporal variations; Underserved populations; Young Adult; Knee health; Data augmentation; Knee sounds; Machine learning

The characteristics of joint acoustic emissions (JAEs) measured from the knee have been shown to contain information regarding underlying joint health. Researchers have developed methods to process JAE measurements and combined them with machine learning algorithms for knee injury diagnosis. While t...