## 3 Results and Discussion  
Kinematic (**X** matrix), sociodemographic, and clinical data (**Y** matrix) were structured in a single database in order to identify the relationships between the two datasets. In this study, the analyses were carried out using mainly the package MixOmics of R.  
We evaluated the statistical significance of the RCCA. The \(p\)-value for Pillai's trace statistic is \(<0.001\). The first two canonical correlations values corresponding to the first two dimensions are equal to 0.80 and 0.79, respectively. These high correlation values show that we can consider a multivariate correlation model and that two dimensions are sufficient to understand the association between the two sets of variables. For the graphical outputs, we empirically adjust a threshold value to 0.35 such that we can highlight the most important correlations between the two sets of data **X** and **Y**. The value of the threshold has been fixed to 0.35 on the basis of the graphical representation of canonical variable correlation (Figure 1). Indeed, most of the variable points are located around the inner-correlation circle with cut-off equal to 0.5. Therefore, we fixed the correlation threshold below this value, i.e., 0.35. \begin{table}
\begin{tabular}{c l c} \hline \hline \(\mathbf{Y}\) & **Variable Description and Range** & **Mean (SD)** \\ \hline \(\mathbf{Y_{1}}\) & Age (years) (40 to 90) & 64.75 (8.89) \\ \(\mathbf{Y_{2}}\) & Gender (0:Women; 1:Men) & Women: n = 83; Men: n = 60 \\ \(\mathbf{Y_{3}}\) & BMI-Body Mass Index (kg/m\({}^{2}\)) (21.8 to 58.5) & 32.37 (6.05) \\ \(\mathbf{Y_{4}}\) & Measure of health-related quality of life (0.03 to 1) & 0.61 (0.22) \\ \(\mathbf{Y_{5}}\) & Visual Analogue Scale for Pain (0 to 95) & 44.34 (24.60) \\ \hline \(\mathbf{Y_{6}}\) & Degree of end-stage OA severity (0:Moderate; 1:Severe) & (Moderate: n = 71; severe: n = 72) \\ \hline \(\mathbf{Y_{7}}\)\(\ldots\)**,18** & Q1, \(\ldots\), Q12 Oxford Question 1 to 12 from Oxford & 1.75 to 3.81 (0.82 to 0.76) \\  & Questionnaire (1 to 5) & 33.15 (9.12) \\ \(\mathbf{Y_{19}}\) & Total Oxford Score: Normalized overall Oxford score (15 to 54) & 33.15 (9.12) \\ \hline \(\mathbf{Y_{20}}\)\(\ldots\)**,32** & Q1,\(\ldots\), Q32 PCS: Question 1 to 13 from PCS Questionnaire & 0.61 to 1.38 (1.01 to 1.33) \\ \(\mathbf{Y_{33}}\) & Total PCS score: Normalized overall PCS score (0 to 52) & 13.14 (13.22) \\ \(\mathbf{Y_{34}}\) & Rumination score related to depression (0 to 16) & 4.43 (4.58) \\ \(\mathbf{Y_{35}}\) & Magnification: Measure of enlargement (0 to 12) & 2.98 (3.14) \\ \(X_{36}\) & Helplessness: Measure of Helplessness-related quality of life & (5.85) (6.11) \\ \hline \(\mathbf{Y_{37}}\) & Index Joint Flex: knee flexion ROM (Range Of Motion) & 123.30 (12.15) \\ \(\mathbf{Y_{38}}\) & Index Joint Ext: knee extension ROM (\(-\)29 to 11) & \(-\)7.75 (7.02) \\ \(\mathbf{Y_{39}}\) & 4 metres Walk (S): Time taken to walk 4 meters, Gait speed in & 3.40 (1.25) \\ \(\mathbf{Y_{40}}\) & Timed Up and Go (S): The progress of balance, sit to stand, & 9.21 (3.08) \\  & and walking (4.7 to 20.9) & 9.36 (3.73) \\ \(\mathbf{Y_{41}}\) & 30-s Chair (Reps): Sit-to-stand repetitions (0 to 20) & 9.36 (3.73) \\ \(\mathbf{Y_{42}}\) & Frailty Index: Clinical Frailty Scale (CFS) (1 to 6) & 2.68 (1.12) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the clinical parameters. The representation of correlation and cross-correlation variable are displayed in Figures 1-3. We distinguish and deduce three clusters (C1, C2, and C3) of highly correlated variables of both sets of data, i.e., each cluster regroups the variables \(\mathbf{X}\) (kinematic measurements) and the variables \(\mathbf{Y}\) (clinical parameters). The clusters are visualized in Figure 1, only the variables that are closely located inside the circle of radius between 0.35 and 1 are represented. The clusters C1 and C2 regroup subsets of parameters from \(\mathbf{X}\) and \(\mathbf{Y}\) variables. C3 regroups the variables \(Y3\) and \(X46\) which are highly and positively correlated.