# A Review of the Vibration Arthography Technique Applied to the Knee Diagnostics
## 4 Conclusions and Perspectives  
The study of sounds emitted from knee joints began a long time ago with the stethoscope and has evolved thanks to technological changes. These advances have allowed the discovery of new ways to record and analyze these sounds. Several techniques are currently available to explore the characteristics of vibroarthrographic signals, such as Fourier or wavelet transform. This review paper mentions some of the parameters that can be extracted from these signals in their time and/or frequency domain. Due to the nature of vibroarthographic signals, the most appropriate analysis technique seems to be the time-frequency one, as it enables the study of the frequencies contained in the signal while preserving the signal's information over time. The vibration arthrography technique has been demonstrated to be a useful and discriminant diagnostic tool. It enables the differentiation of healthy knees from several pathologies such as osteoarthritis. Classification achieved impressive results, with an accuracy ranging from 81.52% to 91.4% according to the signal analysis techniques used [2, 16, 24, 27, 35, 36]. In the same way, it has shown significant results in discriminating spondylorthopathy and rheumatoid arthritis patients [19]. It is worth mentioning some of the major limitations of the vibration arthrography technique that should be addressed in the future. First, the anatomy is highly different between individuals, thus inducing complexity in positioning the sensors on the skin. Second, the experimental setup always produces artifacts either from the sensors themselves (electrical) or from the choice of fixation to the skin (friction). Recently, alternative experimental methods have been developed to avoid some artifacts. A typical example explored by [PERSON] et al. [28] is an instrumented glove with a miniature accelerometer mounted at the fingertips. It has been demonstrated that this method produces reliable results. Furthermore, it could prevent the use of tapes and adhesive pads, thus removing some noise. Such gloves could enable parents of children with juvenile idiopathic arthritis to collect easily vibrational data at home [28]. The vibroarthrography seems to work sufficiently well in discriminating normal and abnormal knees. Yet, the exact source and cause of knee joint vibration emissions are \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors of Research Study** & **Population** & **Processing Technique:** & **Aim of the Study and** \\  & & **Extracted Features** & **Results** \\ \hline  & & TD analysis (segmentation): & Provide a visual tool for \\ [PERSON] et al. (2009) [14] & 11 H—10 OA & amplitude, duration & differentiate healthy and OA \\  & & FD analysis (Fourier & knees \\  & & Transform): peak frequency & \\ \hline [PERSON] et al. (2009) [16] & 20 H—11 OA & TFD analysis: EP, ESP, FP, FSP & Classification of normal and \\  & & & OA knees (accuracy of **91.4\%**) \\ \hline  & & TD analysis: VMS and & Compare the impact of \\ [PERSON] (2014) [17] & 64 H—86 knees with disorders & amplitude & chondomalacia, lateral \\  & & TFD analysis (STFT), partial & patellar compression \\  & & sum of the power spectrum & syndrome and OA on knee \\ \hline  & & TD analysis (segmentation): S, & \\ [PERSON] (2015) [2] & 19 H—20 OA & K, E, TC & Classification of normal and \\  & & TFD analysis (Wavelet & OA knees using a k-NN \\  & & Transform) & classifier (accuracy of **89\%**) \\ \hline  & & TD analysis: segmentation & \\ [PERSON] et al. (2018) [27] & 30 H—39 OA & Frequency domain: partial & Classification of normal and \\  & & sum of the power spectrum & OA knees using an SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. still unknown since this articulation is highly complex and the vibration arthrography technique does not allow us to see the inside of the body. Therefore, future research could focus on biomechanical modeling of the knee to come to a further understanding of the mechanisms behind VAG signals.