# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### Discussion  
In this study, we proposed a multimodal fall detection system using a dataset that combines skeleton, motion, and sensor data. The performance of the multimodal system surpasses that of only the skeleton or sensor data. The accuracy of the multimodal system improved due to the effective integration of features from the skeleton, motion, and sensor modalities. Tables 4 and 6 show the performance accuracy on the UP-Fall and UR-Fall datasets. Additionally, Tables 5 and 7 compare the proposed model with state-of-the-art systems, demonstrating that our model achieves higher performance accuracy than existing methods. One key limitation identified is the dataset imbalance, where fall actions represent only 20% of the total samples in the UP-Fall dataset. The GSTCAN model, which utilizes skeleton data, showed lower accuracy for fall detection on the UP-Fall dataset, as shown in Table 2, especially for activities like walking, sitting, and standing. However, the model demonstrated high accuracy on the UR-Fall dataset, as shown in Table 3. Reviewing the fall videos, we observed that nearly half of the incidents were motionless or involved lying down. This suggests a need for more diverse fall scenarios to improve the model's recognition capability. On the other hand, the accuracy of the accelerometer for actions like standing and jumping was almost 100%, as shown in Table 2, due to the distinct and consistent changes in sensor values, which made these actions easier for the model to recognize. The sudden acceleration changes during falls also contributed to the higher accuracy in the sensor stream compared to skeleton data. However, the recognition accuracy for actions such as falling over or grabbing something using hands and knees was lower, likely due to the gentler acceleration changes during these actions. These subtler changes are harder for the model to detect, leading to misclassification. To improve the accuracy of the multimodal system on the UP-Fall dataset, it is necessary to increase the amount of skeleton data for falls. Future experiments will focus on incorporating additional features, such as the distance between joints, alongside the existing joint movement features, to capture the dynamics of fall incidents better. In addition to addressing the dataset imbalance and improving recognition performance, we plan to explore synthetic data generation techniques. Although we initially refrained from applying SMOTE (Synthetic Minority Over-sampling Technique) due to the complexity of handling video data and preserving temporal dependencies, we recognize its potential for balancing class distribution. In future work, we aim to investigate the application of SMOTE to generate additional fall samples while maintaining the integrity of the temporal relationships in video sequences. Additionally, we will explore complementary approaches, such as class-weight adjustments and further data augmentation techniques, including temporal dependency and synthetic video generation, to address the imbalance and improve the model's robustness.