Assessment of knee joint abnormality using Acoustic Emission sensors

Sarillee, Mohamed; Hariharan, M.; Anas, M.N.; Omar, Mohammad Iqbal; Aishah, M.N.; Oung, Q.W.

doi:10.1109/iccsce.2014.7072748

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The results obtained from statistical parameters comprising of AE events, peak magnitude and average signal level showed that OA knees produced consistently and significantly more AE events with higher peak magnitude and average signal level values than healthy knees. In Sarillee and co‐worker's study , discrimination was done using wavelet packet transform. They obtained an accuracy of more than 80% after principal component analysis using a feed forward neural network and support vector machine.…”

Section: Discussionmentioning

confidence: 99%

“…Since no studies have yet been conducted to evaluate the effectiveness of this technique in controlled environment to diagnose OA, the aim of the current study was to provide a novel alternative method to detect OA, even at a mild stage of the condition. The AE technique has previously been reported for detecting OA in human knees , and OA knees produced consistently and significantly more AE events with higher amplitudes and longer duration than healthy knees . In Shark and co‐worker's study , the discrimination was done using the four‐phase model of sit‐stand‐sit movement.…”

Section: Discussionmentioning

confidence: 99%

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Detection of experimental cartilage damage with acoustic emissions technique: An in vitro equine study

Shakya

Tiulpin

Saarakkala

et al. 2019

Equine Veterinary Journal

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SummaryBackgroundIn horses, osteoarthritis (OA) mostly affects metacarpophalangeal and metatarsophalangeal (fetlock) joints. The current modalities used for diagnosis of equine limb disorders lack ability to detect early OA. Here, we propose a new alternative approach to assess experimental cartilage damage in fetlock joint using Acoustic Emissions (AE).ObjectivesTo evaluate the potential of AE technique in diagnosing OA and see how AE signals changes with increasing severity of OA.Study designAn in vitro experimental study.MethodsA total of 16 distal limbs (8 forelimbs and 8 hindlimbs) from six Finn horses were collected from an abattoir and fitted in a custom‐made frame allowing fetlock joint bending. Eight fetlock joints were opened, and cartilage surface was progressively damaged mechanically three times using sandpaper to mimic mild, moderate and severe OA. The remaining eight fetlock joints were opened and closed without any mechanical procedure, serving as controls. Before cartilage alteration, synovial fluid was aspirated, mixed with phosphate‐buffered saline solution, and then reinjected before suturing for constant joint lubrication. For each simulated condition of OA severity, a force was applied to the frame and then released to mimic joint flexion and extension. AE signals were acquired using air microphones.ResultsA strong association was found between the joint condition and the power of AE signals analysed in 1.5–6 kHz range. The signal from both forelimb and hindlimb joints followed a similar pattern for increased cartilage damage. There were statistically significant differences between each joint condition progressively (generalised linear mixed model, P<0.001) in limbs with in vitro cartilage damage of varying severity while the control limbs did not show any changes.Main limitationsSmall sample size using in vitro, mechanically induced cartilage damage.ConclusionThe AE technique presented here could differentiate the severity of fetlock joint cartilage damage. The consistent results for each simulated condition suggest there is potential for this method in the diagnosis of OA.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Detection of experimental cartilage damage with acoustic emissions technique: An in vitro equine study

Shakya

Tiulpin

Saarakkala

et al. 2019

Equine Veterinary Journal

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“…Several groups of researchers achieved over 90 percent accuracy in the detection of osteoarthritic changes in knee joints with the use of acoustic signal analysis [ 31 ]. In recent decades, numerous research groups have developed this method of evaluating articular cartilage on the basis of both acoustic [ 32 , 33 , 34 , 35 , 36 ] and vibrational signals [ 37 , 38 , 39 , 40 , 41 ]. Despite the long history of the use of vibroacoustic assessment, no clear criteria have emerged for the use of this method in widespread clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint

Karpiński

Krakowski

Jonak

et al. 2022

Sensors

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Osteoarthritis (OA) is a chronic, progressive disease which has over 300 million cases each year. Some of the main symptoms of OA are pain, restriction of joint motion and stiffness of the joint. Early diagnosis and treatment can prolong painless joint function. Vibroarthrography (VAG) is a cheap, reproducible, non-invasive and easy-to-use tool which can be implemented in the diagnostic route. The aim of this study was to establish diagnostic accuracy and to identify the most accurate signal processing method for the detection of OA in knee joints. In this study, we have enrolled a total of 67 patients, 34 in a study group and 33 in a control group. All patients in the study group were referred for surgical treatment due to intraarticular lesions, and the control group consisted of healthy individuals without knee symptoms. Cartilage status was assessed during surgery according to the International Cartilage Repair Society (ICRS) and vibroarthrography was performed one day prior to surgery in the study group. Vibroarthrography was performed in an open and closed kinematic chain for the involved knees in the study and control group. Signals were acquired by two sensors placed on the medial and lateral joint line. Using the neighbourhood component analysis (NCA) algorithm, the selection of optimal signal measures was performed. Classification using artificial neural networks was performed for three variants: I—open kinetic chain, II—closed kinetic chain, and III—open and closed kinetic chain. Vibroarthrography showed high diagnostic accuracy in determining healthy cartilage from cartilage lesions, and the number of repetitions during examination can be reduced only to closed kinematic chain.

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“…Such sensing modalities include the measurement of joint acoustics, edema, and activity via miniature microphones, electrical bioimpedance (EBI) circuitry, and inertial measurement units (IMUs), respectively. For example, joint acoustical emissions ("joint sounds") have been used to discriminate healthy subjects and those with osteoarthritis [1], [2], classify varying conditions of the patellofemoral joint [3], monitor improvements in kids with juvenile idiopathic arthritis after receiving effective medication [4], track rehabilitation improvements in athletes following an acute injury [5], and show changes in loading stresses on the joint [6]. Similarly, EBI has been used to detect swelling (edema) of the joint [7], and studies have demonstrated its efficacy for detecting changes in edema during injury recovery [8].…”

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confidence: 99%

A Wearable, Multimodal Sensing System to Monitor Knee Joint Health

et al. 2020

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Objective: We designed and validated a wearable, multimodal sensor brace for knee joint health assessment. Methods: An embedded-, two-microcontrollerbased approach is used to sample high-throughput, multi-microphone joint acoustics (46.875 kHz) as well as lower-rate electrical bioimpedance (EBI) (1/46.17 s), inertial (100-250 Hz), and skin temperature (1 Hz) data, and these data are saved onto microSD cards. Additionally, a flexible, 3D-printed brace houses the custom circuit boards and sensors to enable wearable sensing. Results: The system achieves 9 hours of continuous joint sound recording, while the EBI, inertial, and temperature sensors can sample for 35 hours using 500 mAh batteries. Further, for the entirety of these continuous recordings, there were no dropped samples for any of the sensors. Lastly, proof-of-concept measurements were used to show the system's efficacy for recording joint sounds and swelling data. Conclusion: This is, to the best of our knowledge, the first, completely untethered wearable system for multimodal knee health monitoring. Significance: The proposed smart brace may facilitate in-clinic or at-home measurements for joint health assessment.

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Assessment of knee joint abnormality using Acoustic Emission sensors

Cited by 5 publications

References 12 publications

Detection of experimental cartilage damage with acoustic emissions technique: An in vitro equine study

Detection of experimental cartilage damage with acoustic emissions technique: An in vitro equine study

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint

A Wearable, Multimodal Sensing System to Monitor Knee Joint Health

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