Reliability of Vibroarthrography to Assess Knee Joint Sounds in Motion

Kalo, Kristin; Niederer, Daniel; Sus, Rainer; Sohrabi, Keywan; Groß, Volker; Vogt, Lutz

doi:10.3390/s20071998

Cited by 13 publications

(30 citation statements)

References 26 publications

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“…The SEm was the multiplied by 1.96 to give a confidence bound, which was added to the top and bottom of the measurement (JS Divergence or SNR) to give a 95% CI. The coefficient of variation (CV) was also calculated for each feature at each location and exercise to assess intersession and intrasession variability, where low levels of variation are defined as CV values less than 12% [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

“…Since the wrist joint is also commonly afflicted with joint injuries and chronic joint disorders, it is necessary to assess the reliability of using JAEs to aid in wrist joint health assessment as explored around the knee [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. To demonstrate JAE recording reliability, JAEs from healthy volunteers can be used [ 19 ]. Many studies focusing on the knee joint selected microphone locations 2 cm lateral and medial to the patellar tendon as the best locations to monitor knee JAEs due to the lower acoustic impedance in the route from the knee joint articulating surfaces to the skin [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies focusing on the knee joint selected microphone locations 2 cm lateral and medial to the patellar tendon as the best locations to monitor knee JAEs due to the lower acoustic impedance in the route from the knee joint articulating surfaces to the skin [ 6 ]. Another study assessed reliability of recording knee JAEs from the tibial plateau and the top of the patella and found those locations to be highly repeatable within recording sessions but had poor intersession repeatability [ 19 ]. Similar to these studies, a location or set of locations known to repeatably record wrist JAEs are needed to monitor the JAEs produced by the wrist and facilitate future design of wearable JAE monitoring systems in a similar manner to other studies which explore sensing modalities at locations around the wrist or knee [ 14 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previous JAE studies around the knee have also explored prescribing different motions to excite JAEs in different manners. Examples of such motion which have been prescribed in previous studies includes unloaded knee flexion-extension, as well as exercises which introduce higher loads on the knee such as sit-to-stand, squat, stair climbing, and vertical leg press [ 8 , 15 , 16 , 18 , 19 , 21 ]. A study of JAEs rooting from the wrist joint should also determine what the best exercises are to prescribe to patients for reliably assessing wrist health.…”

Section: Introductionmentioning

confidence: 99%

“…A study of JAEs rooting from the wrist joint should also determine what the best exercises are to prescribe to patients for reliably assessing wrist health. This study seeks to use signal strength and repeatability levels [ 10 , 19 ] evaluated using nine audio features to assess JAE recording quality from a set of healthy volunteers performing two different exercises at eight different anatomically and experimentally determined locations around the wrist.…”

Section: Introductionmentioning

confidence: 99%

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A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

Hochman

Gharehbaghi

Whittingslow

et al. 2020

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

Hochman

Gharehbaghi

Whittingslow

et al. 2020

Sensors

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Exploration of Coarse-Graining and Threshold Selection of Lempel–Ziv Complexity on Vibroarthrographic Signals

Rameesa Mol,

John

2024

Lecture Notes in Networks and Systems

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RETRACTED ARTICLE: Machine learning based identification and classification of disorders in human knee joint – computational approach

Balajee

Venkatesan

2021

Soft Comput

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Earlier identification of knee joint pathology helps the therapist to provide the appropriate clinical procedures to control the deteriorating process of arthritis. Beyond usual medical investigations, computational techniques have been used for the diagnosis of knee joint disorder. Among different methodologies, Vibroarthrographic technique is employed to identify knee joint disorder. Machine Learning contains number of classification methods for the given data. A novel technique called Greedy sequential backward feature selection based Radial kernelized least square support vector classification (GSBFS-RKLSSVC) is introduced for accurate detection of knee joint pathology with minimum time. The proposed GSBFS-RKLSSVC technique consists of three processes namely feature selection, feature evaluation and classification. Initially number of VAG signal images are taken from the dataset for detection of knee joint disorder. The relevant feature is selected through the Greedy mutual informative regressed sequential backward selection algorithm to reduce an initial dimensional feature space into a low dimensional feature subspace. Following this the dichotomous logit regression is applied to select the best features and discard others. Therefore, the feature selection process of the proposed GSBFS-RKLSSVC minimizes the time consumption of the knee joint pathology detection. Once the signal features are extracted, RKLSSVC is applied to detect the normal and abnormal VAG signal. Decision boundary is utilized by the classifier to categorize the samples based on the similarity between the training features and testing features. As a result, the accurate classification is obtained with a minimum error rate. The observed result indicates that GSBFS-RKLSSVC achieves higher accuracy, sensitivity, specificity and reduces time than the conventional methods.

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Reliability of Vibroarthrography to Assess Knee Joint Sounds in Motion

Cited by 13 publications

References 26 publications

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

Exploration of Coarse-Graining and Threshold Selection of Lempel–Ziv Complexity on Vibroarthrographic Signals

RETRACTED ARTICLE: Machine learning based identification and classification of disorders in human knee joint – computational approach

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