Abstract
This paper explores the novel application of an automated b-value extraction algorithm for the interpretation of sounds produced by the knee joint during movement. Acoustical emissions were recorded from a total of eight subjects with acute knee injuries a first time, within one week of the injury, then a second time, four to six months following corrective surgery and rehabilitation. The data were collected from each subject using miniature electret microphones placed on the medial and lateral side of the patella during knee flexion and extension exercises. From the acoustical signals measured from each subject, we computed the b-value using the modified Gutenberg-Ritcher equation which is widely used in seismology. The b-value increased for each subject's injured knee from immediately following the injury to several months post recovery. (mean b-value: 1.46 ± 0.35 [injured] and 1.92 ± 0.21 [post-surgery and recovery], p < 0.01). In addition, we compared this analysis technique against an unsupervised machine learning algorithm from our previous work and found that the b-value metric can be as effective to differentiate changes in the joint sounds as our prior approach while requiring less computational time and complexity - both of which are preferable for future integration of this technology into a wearable system.