Knee Joint Vibroarthrographic Signal Processing and Analysis

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Sign In. Access provided by: anon Sign Out. Classification of knee-joint vibroarthrographic signals using time-domain and time-frequency domain features and least-squares support vector machine Abstract: Analysis of knee-joint vibration sounds, also known as vibroarthrographic VAG signals, could lead to a noninvasive clinical tool for early detection of knee-joint pathology.

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In this paper, we employed the wavelet matching pursuit MP decomposition and signal variability for time-frequency domain and time-domain analysis of VAG signals. The number of wavelet MP atoms and the number of significant turns detected with the fixed threshold from signal variability analysis were extracted as prominent features for the classification over the data set of 89 VAG signals.

Compared with the Fisher linear discriminant analysis, the nonlinear least-squares support vector machine LS-SVM is able to achieve higher overall accuracy of Article :.

PL EN. Widoczny [Schowaj] Abstrakt.

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Adres strony. Applied Computer Science.

Application of acoustic signal processing methods in detecting differences between open and closed kinematic chain movement for the knee joint. The paper presents results of preliminary research of analysis of signals recorded for open and closed kinematic chain in one volunteer with chondromalacia in both knees.

The preliminary research was conducted in order to establish the accuracy of the proposed method and will be used for formulating further research areas. The aim of the paper is to show how FFT, recurrence plots and recurrence quantification analysis RQA can help in bioacoustic signals analysis. Opis fizyczny.

Knee Joint Vibroarthrographic Signal Processing and Analysis

Machrowska, Anna. Maciejewski, Marcin. Knee joint biomechanics in closed-kinetic-chain exercises. Computer Methods in Biomechanics and Biomedical Engineering, 12 6 , — Analysis of patellofemoral arthrokinematic motion quality in open and closed kinetic chains using vibroarthrography. BMC Musculoskeletal Disorders, 20 1.

Joint motion quality in vibroacoustic signal analysis for patients with patellofemoral joint disorders.

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Details of data acquisition may be found in Krishnan et al. Write your review. Referring to Table 1 and Figure 5 , the superiority of the DWF-based ensemble in the diagnostic performance was prominent. To overcome such a drawback, the lowpass Butterworth filter and the fixed threshold method were introduced in the present study. The portal can access those files and use them to remember the user's data, such as their chosen settings screen view, interface language, etc. Auscultation of the knee joint using a stethoscope was also performed, and a qualitative description of the sound intensity and type was recorded.

BMC Musculoskeletal Disorders, 15, Sex differences in the skeletal geometry of the human pelvis and hip joint. Journal of Biomechanics, 14 6 , — Multiscale recurrence analysis of long-term nonlinear and nonstationary time series.

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This book presents the cutting-edge technologies of knee joint vibroarthrographic signal analysis for the screening and detection of knee joint injuries. Request PDF on ResearchGate | Knee Joint Vibroarthrographic Signal Processing and Analysis | This book presents the cutting-edge technologies of knee joint.

Sensors and Materials, 30 8 , Gender differences in vertebral sizes in adults: biomechanical implications. Radiology, 3 , — Stress distribution in the knee joint in relation to tibiofemoral angle using the finite element method. An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis.

Knee Joint Vibroarthrographic Signal Processing and Analysis

Computer Methods and Programs in Biomedicine, 94 2 , — Psoriatic arthritis — new perspectives. Archives of Medical Science. Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals. Computer Methods and Programs in Biomedicine, , 37—

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