Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Ołowiana, Ewelina; Selkow, Noelle M.; Laudner, Kevin G.; Puciato, Daniel; Bączkowicz, Dawid

doi:10.1186/s13102-020-00201-z

Cited by 8 publications

(7 citation statements)

References 47 publications

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“…Bolus et al [28] propose measurement in an inverted squat-like position with the knee loaded by a given force. A similar setup with traditional squats was used by Olowiana et al [30]. Kalo et al used either sit-ups in [11] or climbing up and going down the stairs in [31].…”

Section: Experimental Methodsmentioning

confidence: 99%

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A Review of the Vibration Arthrography Technique Applied to the Knee Diagnostics

2021

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When a joint undergoes a range of motion, its constituents rub against each other, causing friction and thus vibrations. The vibration arthrography (VAG) technique consists of detecting, recording, and processing those vibrations to diagnose disorders. This non-invasive method could be an alternative to the currently used arthroscopy or X-rays. It has been revealed to be as accurate as these methods in terms of detecting pathologies such as osteoarthritis (OA). Moreover, vibrational analysis has highlighted some physiological signals associated with the displacement of knee joint components. Thus, vibroarthrography provides further understanding of the internal mechanisms of the knee joint. This paper aims to examine the research progress on the use of this vibration arthrography technique in diagnosing knee disorders.

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Section: Experimental Methodsmentioning

confidence: 99%

“…In addition, the amplitude and duration of the signal have shown encouraging results in differentiating OA and healthy knee joints [14]. Similarly, Olowiana et al [30] have estimated the variability of the VAG signal by computing the variance of the mean-squared values and the signal amplitude.…”

Section: Time Domain Analysismentioning

confidence: 99%

A Review of the Vibration Arthrography Technique Applied to the Knee Diagnostics

2021

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“…The patellofemoral joint (PFJ) plays a very important role in the knee extensor mechanism. Its part—the patella—is known as the largest sesamoid bone, which acts as a shield for the anterior trochlea and protects it against excessive friction between the quadriceps tendon and the femoral condyles [ 28 , 29 , 30 , 31 , 32 ]. As far as joint motion assessment is concerned, one can distinguish some quantitative and qualitative methods [ 25 , 28 , 33 , 34 , 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Using Nonlinear Vibroartrographic Parameters for Age-Related Changes Assessment in Knee Arthrokinematics

Kręcisz

Bączkowicz

Kawala-Sterniuk

2022

Sensors

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Changes in articular surfaces can be associated with the aging process and as such may lead to quantitative and qualitative impairment of joint motion. This study is aiming to evaluate the age-related quality of the knee joint arthrokinematic motion using nonlinear parameters of the vibroarthrographic (VAG) signal. To analyse the age-related quality of the patellofemoral joint (PFJ), motion vibroarthrography was used. The data that were subject to analysis represent 220 participants divided into five age groups. The VAG signals were acquired during flexion/extension knee motion and described with the following nonlinear parameters: recurrence rate (RR) and multi-scale entropy (MSE). RR and MSE decrease almost in a linear way with age (main effects of group p<0.001; means (SD): RR=0.101(0.057)−0.020(0.017); and MSE=20.9(8.56)−13.6(6.24)). The RR post-hoc analysis showed that there were statistically significant differences (p<0.01) in all comparisons with the exception of the 5th–6th life decade. For MSE, statistically significant differences (p<0.01) occurred for: 3rd–7th, 4th–7th, 5th–7th and 6th life decades. Our results imply that degenerative age-related changes are associated with lower repeatability, greater heterogeneity in state space dynamics, and greater regularity in the time domain of VAG signal. In comparison with linear VAG measures, our results provide additional information about the nature of changes of the vibration dynamics of PFJ motion with age.

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“…However, the designed method failed to integrate both temporal and frequency features to improve the accuracy. The vibroarthrographic evaluation of changes in arthrokinematics was analyzed in Ołowiana et al (2020) with variated loads and correlation between the features. But an efficient technique was not implemented to find exact features for minimizing the complexity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Identification and Classification of Disorders in human Knee Joint - Computational Approach

Rajagopal¹

2021

Preprint

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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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Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads

Cited by 8 publications

References 47 publications

A Review of the Vibration Arthrography Technique Applied to the Knee Diagnostics

A Review of the Vibration Arthrography Technique Applied to the Knee Diagnostics

Using Nonlinear Vibroartrographic Parameters for Age-Related Changes Assessment in Knee Arthrokinematics

Machine Learning based Identification and Classification of Disorders in human Knee Joint - Computational Approach

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