ArticleIEEE transactions on bio-medical engineering2022
A Feasibility Study on Tribological Origins of Knee Acoustic Emissions.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment.Sensors (Basel, Switzerland) · 2025Article
- Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2024Article
- Validating Joint Acoustic Emissions Models as a Generalizable Predictor of Joint Health.IEEE sensors journal · 2024Article
- Classifying Pre-Radiographic Osteoarthritis of the Knee Using Wearable Acoustics Sensing at the Point of Care.IEEE sensors journal · 2023Article
- Knee acoustic emissions as a noninvasive biomarker of articular health in patients with juvenile idiopathic arthritis: a clinical validation in an extended study population.Pediatric rheumatology online journal · 2023Article
- On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
objectiveConsidering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs). The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that JAEs are attributed to tribological origins.
methodsJAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from ten healthy subjects while performing two-leg and one-leg squat exercises. The biomechanical signals were processed to calculate a tribological parameter, lubrication coefficient, and JAEs were divided into short windows and processed to extract 64-time-frequency features. The lubrication coefficients and JAE features of two-leg squats were used to label the windows and train a classifier that discriminates the knee lubrication modes only based on JAE features.
resultsThe classifier was used to predict the label of one-leg squat JAE windows and it achieved a high test-accuracy of 84%. The Pearson correlation coefficient between the estimated friction coefficient and predicted JAE scores was 0.83 ± 0.08. Furthermore, the lubrication coefficient threshold, separating two lubrication modes, decreased by half from two-leg to one-leg squats. This result was consistent with tribological changes in the knee load as it was inversely doubled in one-leg squats. SIGNIFICANCE: This study supports the potential use of JAEs as a quantitative biomarker to extract tribological information. Since arthritis and similar disease impact the roughness of the joint cartilage, the use of JAEs could have broad implications for studying joint frictions and monitoring joint structural changes with wearable devices.
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