## 2 Related Work
### Advances in Deep Learning for Fall Risk Prediction
The simplicity of the setup, using only two wearable sensors during a one-minute walking task, underscores the potential of using deep learning in combination with wearable technologies for practical and effective fall risk assessment. The application of deep learning for fall risk assessment using inertial sensors was also explored, specifically focusing on spatiotemporal gait parameters. The study emphasizes the value of domain knowledge in improving model performance, as spatiotemporal gait metrics are critical for accurately predicting fall risk. The authors used long short-term memory (LSTM) neural networks to process gait data, demonstrating that incorporating domain-specific features significantly enhances the predictive power of deep learning models in fall risk assessment [64]. The use of wrist-worn inertial sensors and deep learning techniques to detect gait abnormalities associated with fall risk was investigated. The authors employed a combination of convolutional layers and bidirectional long short-term memory (BiLSTM) layers to capture spatiotemporal features from accelerometer, gyroscope, and rotation vector sensor data collected from smartwatches worn on both wrists. Their proposed model achieved an accuracy of 88.9%, sensitivity of 90.6%, and specificity of 86.2%, demonstrating its effectiveness in detecting abnormal gait patterns. This study highlighted the potential of wearable sensors and deep learning for continuous and non-invasive fall risk assessment, which aligns with our aim of leveraging deep learning models for fall detection and prevention [65]. Finally, [PERSON] [66] explored the use of deep learning techniques to assess fall risk by classifying walking surface conditions using IMU-based gait data. A 25-layer convolutional neural network was developed to classify nine different walking surfaces, emphasizing the influence of environmental factors on fall risk. The study collected data from 30 participants wearing six IMU sensors, achieving accuracies of up to 0.971 under optimal conditions. This research highlighted the importance of considering environmental context in fall risk assessment, which complements our study's focus on deep learning models for identifying fall risk through gait and postural control data. Despite the advancements in fall risk assessment, there remains a significant gap in the real-time, continuous, and non-invasive evaluation of fall risk, particularly using wearable technologies. Existing clinical tools, while useful in controlled environments, often fail to capture the dynamic nature of a fall risk in real-world settings, and they many rely on subjective assessments that can introduce variability and bias. Furthermore, while many machine learning and deep learning techniques have been applied to fall risk prediction, few studies have fully explored the potential of combining multisensor data from wearable devices to evaluate balance and postural control across a comprehensive set of functional movements. Our study sought to address this gap by leveraging the clinical robustness of the Mini-BESTest, which is a well-validated tool for assessing balance, and combining it with sensor-based data from inertial measurement units (IMUs). By using deep learning models to process data from multiple tasks of the Mini-BESTest, our approach captured a broader spectrum of movement patterns, thereby improving the predictive power of fall risk. The innovation in this work lies in the ability to fuse data from different functional tasks and provide an integrated fall risk assessment that is accurate and clinically relevant. Unlike traditional methods that rely on single-task assessments, our approach demonstrated the value of using combined signals from multiple postural and gait tasks to improve fall risk prediction.