## 2 Materials and Methods
### Identification of Patients According to the Improvement in Their Condition  
The patients underwent a clinical assessment at baseline and after six months, using the Knee Injury and Osteoarthritis Outcome Score (KOOS) [20]. This questionnaire assesses five domains: the patient's knee pain (9 items), other symptoms (7 items), function in daily life (17 items), sport and recreation (5 items) and knee-related quality of life (4 items). Each one of them can be analyzed individually with a score which ranges from 0 (indicating the worst-case scenario) and 100 (indicating the absence of knee symptomatology).  
Patient improvement was determined based on the KOOS questionnaire. Based on a literature review and a preliminary study [21], the KOOS\({}_{pain}\) subscale was identified as the most representative score and therefore its variation between baseline and 6 months was used to determine whether a patient's condition improved (Class I) or not (Class nI). A growing consensus in the knee OA literature suggests that identifying a single threshold to define a meaningful change in the KOOS\({}_{pain}\) score across varying OA severity and follow-up periods is problematic [22; 23]. Furthermore, a recent meta-analysis [24] supports that  
Figure 2: Block diagram of the adopted methodology.  
estimated thresholds increase along with patient baseline severity, and that relative changes may be a better approach than absolute differences. Based on the authors' conclusions and the variability in our participants' clinical characteristics (KL grades 2, 3, and 4), we considered that a patient's condition was improved if the KOOS\({}_{pain}\) score at 6 months exceeded the baseline score by 10% or more, as this value is both \"intuitive and consistent with estimates for other instruments\" [24].  
Hence, the participants were divided into two classes based on the evolution of their KOOS\({}_{pain}\) between baseline and 6-month follow-up: one class of patients whose condition improved (I) and one class of patients whose condition did not improve (nI).  
In other words, for each patient, the variable \(\eta\) was computed as follows:  
\[\eta=\frac{\text{KOOS}_{pain}(t_{6})-\text{KOOS}_{pain}(t_{0})}{\text{KOOS}_ {pain}(t_{0})}\times 100\]  
and, the assigned condition (class) was then:  
\[\begin{cases}\text{I},&\text{if }\eta\geq 10\%\\ \text{nI},&\text{otherwise}.\end{cases} \tag{1}\]  
Based on this assessment, the participants were categorized according to their improvement status. Tables 1-3 summarize the demographic characteristics of the two classes (i.e., I and nI) for all participants, the male and female population respectively. There was no statistical difference in terms of age and BMI distributions between classes regardless of the population considered (\(T\)-tests: all \(p\) > 0.68). Statistical processing was implemented via SPSS 18.0 (Statistical Package for Social Sciences). A \(p\)-value of 0.05 was set as the criterion for statistical significance.  
in the transverse plane. These curves are mean patterns of multiple gait cycles collected per subject and normalized to a range from 1% to 100% of the gait cycle (GC). The beginning of the GC (1%) corresponds to the heel contact which is identified by the first minimum of the flexion/extension curve (Figure 3).  
A set of 69 biomechanical parameters of interest was then extracted from these 3D kinematic curves for data analysis. The parameters chosen for extraction were based on variables routinely assessed in biomechanical studies of knee OA populations, such as maximums, minimums, varus and valgus thrust, angles at initial contact, mean values and ranges of motion (ROM) throughout GCs or GC sub-phases (i.e., loading, stance, swing, etc.) [25; 26].