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

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

doi:10.21203/rs.3.rs-21513/v3

Cited by 1 publication

(1 citation statement)

References 34 publications

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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%

RETRACTED ARTICLE: Machine learning based identification and classification of disorders in human knee joint – computational approach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

confidence: 99%