## 4 Discussion  
The results reveal that both conventional and G-STRIDE functional evaluations show significant differences between participants with and without falls according to frailty status, falls being more frequent in frail subjects. It is possible to identify gait parameters using the G-STRIDE device that discriminate between fit and frail subjects with and without falls and this is of great interest as it creates the opportunity for early frailty detection. To our knowledge, this is the first study to stratify participants according to both aspects at the same time to identify people at higher risk of adverse events. The results show significant differences between groups according to frailty status and falls and identifies gait parameters explored by the G-STRIDE device that can stratify participants according to frailty status and falls, which is of great interest as it allows for early intervention. The descriptive variables indicate that fit participants without falls are those who walk on mixed terrain, whereas the percentage of frail participants or those with falls that walk on flat terrain increases, consistent with lower walking difficulty on such surfaces. Conventional evaluation results show significant differences in gait speed (GS), TUG and Short-FES1 among the four groups studied. [PERSON]'s review of falls assessment tools finds the TUG and Tinetti Test to be the most appropriate in community settings, \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Class** & **Precision** & **Recall (TPR)** & **F1 Score** & **Specificity** \\ \hline Fit with falls & 0.88 & 0.98 & 0.93 & 0.98 \\ \hline Fit without falls & 0.88 & 0.9 & 0.89 & 0.97 \\ \hline Frail without falls & 1.0 & 0.14 & 0.25 & 1.0 \\ \hline Frail with falls & 0.94 & 0.86 & 0.90 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance metrics for Random Forest model predicting the four categories. Figure 3: Confusion matrix. with a sensitivity \(>0.7\), recommending the use of multiple tools simultaneously to improve falls prediction [44]. A recent review also found that no single gait, balance or functional mobility assessment can predict fall risk in older adults with high certainty, although moderate evidence suggests gait speed can be useful [45]. Regarding the Short-FES1 scale, similar results have been published, with higher scores in subjects with falls relating to self-perception of risk [46; 47]. Current global guidelines for patient care with falls recommend using these three tools with a grade of 1A [18]. The study suggests that variables detected by the G-STRIDE device enhance the ability to distinguish between subjects based on frailty status and fall history, enabling proper differentiation among frail subjects with falls, frail subjects without falls, and fit subjects with or without falls. G-STRIDE quantitative measurements of physical activity (total distance, total time, total number of steps), along with gait speed and step speed, contribute significantly to the model, distinguishing fit from frail subjects. Parameters related to gait characteristics (3D path, swing time, stance-foot flat time, clearance, stride length) contribute to distinguishing between subjects with and without falls. These results support the need for technological data collection systems that enhance clinical evaluation in daily practice. Other studies have used sensor devices under different testing conditions (sensor location, test duration, variables measured) and with different objectives (some evaluating frailty, others focusing on falls) [29; 30; 31; 32]. Many propose mixed models with functional and sensor parameters, generally finding that technological devices improve diagnosis. We found only one study that analyzed both frailty and falls; [PERSON] et al. studied functional and sensor variables in 124 participants to identify a model for classifying participants according to frailty and falls separately, without relating the two variables [48].