Abstract
Joint acoustic emission (JAE) sensing has recently proven to be a viable technique for non-invasive quantification indicating knee joint health. In this work, we adapt the acoustic emission sensing method to measure the JAEs of the wrist—another joint commonly affected by injury and degenerative disease. JAEs of seven healthy volunteers were recorded during wrist flexion-extension and rotation with sensitive uniaxial accelerometers placed at eight locations around the wrist. The acoustic data were bandpass filtered (150 Hz–20 kHz). The signal-to-noise ratio (SNR) was used to quantify the strength of the JAE signals in each recording. Then, nine audio features were extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen–Shannon (JS) divergence were calculated to evaluate the interrater repeatability of the signals. We found that SNR ranged from 4.1 to 9.8 dB, intrasession and intersession ICC values ranged from 0.629 to 0.886, CVs ranged from 0.099 to 0.241, and JS divergence ranged from 0.18 to 0.20, demonstrating high JAE repeatability and signal strength at three locations. The volunteer sample size is not large enough to represent JAE analysis of a larger population, but this work will lay a foundation for future work in using wrist JAEs to aid in diagnosis and treatment tracking of musculoskeletal pathologies and injury in wearable systems.
Highlights
The wrist is one of the most injured joints in athletes, especially in adolescents
We validate that this framework will consistently excite and record high quality Joint acoustic emission (JAE) through confirmation of the ability of the prescribed exercises and microphone locations to excite and record JAEs from the wrist, assessing the impact of noise and motion artifacts, and performing repeatability testing on our measurements of raters that have demonstrated importance to knee joint health classification based on JAEs
The locations proximal to the end of the radius demonstrated higher signal strength through the signal-to-noise ratio (SNR) than other locations around the wrist and the locations tested around the knee, whereas locations P3, D2, and M3 showed a balance of good signal strength and excellent repeatability
Summary
The wrist is one of the most injured joints in athletes, especially in adolescents. Of all adolescents who participate in athletics, 1.3% have sustained wrist injuries via traumatic injuries in contact sports and overuse injuries in golf, racquet sports, and gymnastics [1]. Disabilities of the wrist and hand are the second largest cause of missed workdays [3]. These injuries and chronic joint disorders impact patients’ quality of life and ability to participate in hobbies, athletics, and other activities; these conditions put pressure on health systems, requiring diagnosis and treatment efforts in a large population of patients [4]. The current standard in noninvasive diagnostic tools for such conditions include a combination of (1) imaging—which is expensive—and (2) physical examination, mobility assessments, and patient-reported pain assessments—all of which are subjective to either the patient or physician The weaknesses of these tools are compounded when attempting to track treatment progress, as subjective data is weaker than quantitative data
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