Initial Study of Low Cost Crepitus Joint Degradation Finding Using Acoustic Localization

Current methods of joint diagnosis depend on invasive, radiative methods, large set ups and requiring skilled operators, which are still not able to assess the joint during motion. Replacing these methods are acoustic methods, which are only able to diagnose joint health based on the characteristics sound emitted by a moving joint. This paper looks into informing the medical practitioner of the locations of joint damage instead, hence the feasibility of localizing upon the damaged part of the joint, which is represented by a playback of a recording of crepitus sounds emitted from a human joint in 2 dimensional settings. Three microphones, an earphone and a data acquisition device were used to implement the testing. Using the time-difference-of-arrival (TDOA) between the 3 sensors, the Angle-of-Arrival (AOA) method was used to attain the initial localization coordinates. These coordinates were then fed into a gradient descent algorithm to reduce the positioning error of the AOA method, yielding the final position results. Results show that the developed system is able to achieve localization errors of less than 1.0 cm.

[1]  Rangaraj M. Rangayyan,et al.  Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows , 2010, Biomed. Signal Process. Control..

[2]  R.M. Rangayyan,et al.  Analysis of knee joint sound signals for non-invasive diagnosis of cartilage pathology , 1990, IEEE Engineering in Medicine and Biology Magazine.

[3]  J Goodacre,et al.  Knee acoustic emission: a potential biomarker for quantitative assessment of joint ageing and degeneration. , 2011, Medical engineering & physics.

[4]  H. Schwalbe,et al.  Detection of osteoarthritis using acoustic emission analysis. , 2019, Medical engineering & physics.

[5]  Anita Agrawal,et al.  Time-frequency based feature extraction for the analysis of vibroarthographic signals , 2018, Comput. Electr. Eng..

[6]  A Guermazi,et al.  The association of magnetic resonance imaging (MRI)-detected structural pathology of the knee with crepitus in a population-based cohort with knee pain: the MoDEKO study. , 2010, Osteoarthritis and cartilage.

[7]  Simon Weidert,et al.  Detection and Grading of Knee Joint Cartilage Defect using Multi-Class Classification in Vibroarthrography , 2018 .

[8]  Jin U. Kang,et al.  Enhanced time-frequency analysis of VAG signals by segmentation and denoising algorithm , 2008 .

[9]  Yoshitsugu Kamiya,et al.  A feasibility study of utilizing tribo-acoustics for mobile user interface , 2013, 2013 Seventh International Conference on Sensing Technology (ICST).

[11]  Q. Zou,et al.  Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method. , 2014, Medical engineering & physics.

[12]  I. Sekiya,et al.  Toward classification criteria for early osteoarthritis of the knee. , 2018, Seminars in arthritis and rheumatism.

[13]  Rangaraj M. Rangayyan,et al.  Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology , 2000, IEEE Transactions on Biomedical Engineering.

[14]  R A Mollan,et al.  Artefact encountered by the vibration detection system. , 1983, Journal of biomechanics.

[15]  Hiroaki Seki,et al.  Triboacoustic localization system for mobile device - Environmental effects to accuracy , 2014 .

[16]  G. Cramer,et al.  A Feasibility Study to Assess Vibration and Sound From Zygapophyseal Joints During Motion Before and After Spinal Manipulation , 2017, Journal of manipulative and physiological therapeutics.

[17]  Tsair-Fwu Lee,et al.  Analysis of Vibroarthrographic Signals for Knee Osteoarthritis Diagnosis , 2012, 2012 Sixth International Conference on Genetic and Evolutionary Computing.

[18]  M L Chu,et al.  Detection of knee joint diseases using acoustical pattern recognition technique. , 1976, Journal of biomechanics.

[19]  H. Benabdallah,et al.  Acoustic Emission and Its Relationship with Friction and Wear for Sliding Contact , 2008 .

[20]  Zhe Geng,et al.  Using acoustic emission to characterize friction and wear in dry sliding steel contacts , 2019, Tribology International.

[21]  Tuan V. Nguyen Osteoarthritis in southeast Asia , 2014 .

[22]  Saif Nalband,et al.  Feature selection and classification methodology for the detection of knee-joint disorders , 2016, Comput. Methods Programs Biomed..

[23]  A. Amalin Prince,et al.  Analysis of Knee Joint Vibration Signals Using Ensemble Empirical Mode Decomposition , 2016 .