## 2 Related Work
### Advances in Deep Learning for Fall Risk Prediction  
Deep learning has opened new possibilities for analyzing complex data obtained from inertial sensors. Deep neural networks (DNNs) and convolutional neural networks (CNNs) are effective in identifying complex movement patterns that are not detected by traditional data analysis methods [55; 56]. For example, in a study, researchers used recurrent neural networks (RNNs) to analyze movement patterns obtained through IMUs, achieving significant improvements in fall risk prediction accuracy compared to more traditional methods such as support vector machines and random forests [57]. These advances suggest that the use of deep learning algorithms offers a new approach to improving risk identification. In another study, researchers explored the use of deep neural networks for predicting fall risk by utilizing time series signals generated from a force plate. The study highlighted the capability of neural networks to identify human balance patterns and their effectiveness in classifying individuals based on their fall risk [58]. This investigation is particularly relevant to our work, as both studies share the goal of improving fall prediction through the analysis of complex balance data. The use of force plates and neural network analysis in the context of postural control emphasizes the potential of these approaches to enhance the accuracy of fall risk prediction. [PERSON] and [PERSON] [59] provided a comprehensive survey on deep learning techniques applied to inertial positioning, emphasizing the use of inertial sensors in smartphones, drones, and IoT devices for localization and motion tracking. The survey explores key challenges in inertial positioning, such as sensor calibration, reducing error drifts, and enhancing multisensor fusion. The study highlighted the potential of deep learning to address the limitations of traditional inertial navigation algorithms, particularly in mitigating measurement errors and unbounded drifts that often occur with low-cost MEMS inertial sensors. This research is especially relevant to our work, as it aligns with the use of deep learning to analyze complex sensor data, which is critical for accurately predicting balance and postural control issues in older adults. While other researchers have proposed a Bidirectional Residual Deep Short-Term Memory (Bidir-LSTM) model for human activity recognition (HAR) using wearable sensors [60]. The use of Bidir-LSTM is particularly beneficial in improving the ability to capture temporal dependencies both forward and backward in time. The authors demonstrated that this architecture significantly improved the model's performance on HAR tasks by increasing recognition rates when applied to public datasets such as Opportunity and UCI [61], which explained the effectiveness of using deep learning architectures with wearable sensor data to capture complex motion patterns. In addition, one study explored the use of deep learning and machine learning methods, specifically joint node graphs (JNPs), to assess postural control in younger and older adults. The study used Kinect to capture joint motions and analyzed individuals' postural stability during 40-s standing tasks. Their findings demonstrated that deep learning methods could effectively distinguish between different age groups based on their postural control, with the accuracy, sensitivity, and specificity metrics exceeding 0.9 [62]. This study demonstrated the utility of deep learning techniques to analyze postural control, which aligns with our goals of improving fall risk prediction using wearable sensors and biomechanical data. A deep neural network was applied--specifically, a bidirectional long short-term memory (BiLSTM) network--to classify fall risk in individuals with multiple sclerosis based on gait data captured from wearable sensors. The model demonstrated a strong performance with an AUC of 0.88, significantly improving upon traditional machine learning models based on spatiotemporal gait parameters and other statistical features [63].