Knee joint health assessment using acoustic sensors in osteoarthritis: a quantitative and parametric study.

This paper presents a novel diagnosis approach to sensor-based acoustic emission (AE) for the assessment of the dynamic integrity of the human knee joint, along with its efficacy on healthy and osteoarthritis (OA) subjects. A total of 121 humans with increasing ages from different healthy to OA knee conditions participated in this study. AE hits and other signal parameters, including signal amplitude (measured in decibels), rise time, duration, absolute energy, and signal strength, are analyzed in conjunction with joint angles during sit-to-stand (S-T-S) activity. The analysis is performed across four distinct movement phases to assess variations in knee joint conditions. In healthy subjects, bilateral symmetry in acoustic hits is observed, indicating comparable AE activity in both legs. Acoustic hits specifically refer to the total number of detected AE events during joint movement, providing a key quantitative measure for evaluating OA changes and overall knee joint health. Acoustic hits and signal amplitude showed a significant increase in OA subjects compared to healthy individuals. The statistical evaluation of time and energy signal features revealed a significant difference between healthy and osteoarthritis groups (p < 0.001 at 95% confidence interval (CI) for healthy group 3 andp < 0.001 at 95% CI for the OA group). In OA groups, the signal duration is four times longer (p < 0.001 at 95% CI), and absolute energy is 26 times higher (p < 0.001 at 95% CI) than in healthy subjects (group 3). Here, the statedp-values are obtained from thet-test.Cumulative probability index analysis established a linear and non-linear trend among the groups, and OA subjects are identified with the most deviated feature patterns in AE detection. From the study outcomes, a strong basis is formed for the development of sensor-based wearable systems in the early diagnosis of OA.