## 5 Discussion  
Falls among older adults are a significant public health concern, contributing to high rates of morbidity and mortality in this population. The early identification of individuals at risk of falls is crucial to implementing preventative strategies and reducing the incidence of falls. However, traditional fall risk assessments, though widely used in clinical settings, often lack precision in predicting future falls, particularly when it comes to capturing complex postural adjustments and movement patterns. The primary purpose of this study was to evaluate the effectiveness of using deep learning models in conjunction with inertial sensor data from smartphone-based assessments to improve the accuracy of fall risk prediction. By leveraging the Mini-BESTest functional tasks, we aimed to determine which specific tasks or combined signals are most effective in identifying fall risk among older adults. The findings suggest that the deep learning models performed effectively in tasks such as forward and backward step corrections and gait speed changes, demonstrating high accuracy in detecting fall risk (refer to Table 2 and Figure 5 in the Results section). However, sensory orientation and dual-task gait posed greater challenges, with lower specificity indicating a higher incidence of false positives (see Sections 4.4 and 4.5 for detailed metrics). This demonstrates that while some tasks are highly predictive of fall risk, others may require further refinement of the model to improve discriminatory power. On the other hand, the analysis of the combined signals from all Mini-BESTest tasks demonstrated a more robust and balanced performance compared to evaluating each task individually; see Table 2 and Figure 5). Specifically, the model achieved notable improvements in accuracy (88.55%), precision (90.14%), and F1 score (89.02%) when incorporating data from multiple functional tasks. These metrics improved significantly by leveraging a broader range of movement data, enhancing the model's ability to correctly classify both fallers and non-fallers. The integration of diverse data sources, such as acceleration, angular velocity, and magnetic signals, allowed the model to provide a more comprehensive view of fall risk. This finding underscores the value of fusing signals from different measurement domains to achieve more accurate and reliable predictions. Furthermore, the use of multiple data sources enabled the model to capture the complexities of postural control, gait, and other factors influencing fall risk more deeply. By integrating information from various tasks, the model could identify subtle patterns that might otherwise be overlooked in individual task evaluations. This suggests that approaches based on signal combination have the potential to offer more holistic and accurate assessments compared to models that analyze a single task or metric. In comparison to traditional fall risk prediction approaches such as logistic regression, decision trees, and random forests, the proposed deep learning model offers significant advantages in capturing complex movement patterns. For instance, previous studies have shown that logistic regression models achieve an average accuracy of 70-75% in fall risk prediction [55], while decision trees and random forests typically have accuracies in the range of 75-80% [56]. However, these traditional approaches tend to rely on pre-selected variables and show limitations in their ability to capture temporal and non-linear dependencies. In contrast, the use of convolutional neural networks (CNNs) and long short-term memory (LSTM) models allows for automatic feature extraction and a richer representation of temporal sequences, resulting in significant improvements in predictive performance. In this study, the CNN-LSTM model achieved an accuracy of 88.55%, with an F1 score of 89.02%, outperforming methods that focus solely on individual tasks from the Mini-BESTest.