# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 5 Discussion of Limitations and Future Outlook
Optimizing the system architecture to meet various constraints such as memory, processing capability, and energy consumption while finding the optimal balance between detection accuracy and hardware limitations is crucial. During application, collecting long-term motion data for each user to analyze and individualize the parameters within the fall detection model may enhance detection accuracy. Meanwhile, methods using wearable sensor devices seem impractical in PTAs with large crowds, as it is impossible to provide every pedestrian with sensor equipment that requires daily wear. Suitable motion characteristic collection methods need to be found. Additionally, collecting personal motion data poses a risk of invading privacy, and public acceptance must be considered. In the future, as Figure 16 shows, existing fall detection methods can be applied in the field of PTAs and be optimized depending on the complex features of different scenarios. The fusion of multiple sensor data has been proven to better reflect pedestrian motion features than a single sensor. Based on this, integrating various fall detection methods may overcome their limitations. For example, in PTAs with high crowd density, pedestrian occlusions make extracting pedestrian motion features from video data more difficult, which also reduces data reliability. Researchers often borrow solutions from other computer vision research fields (such as object tracking, object detection, etc.). However, sensor-derived motion feature data remain highly reliable in occluded situations, suggesting the combination of computer vision and wearable device methods to ensure data reliability during occlusions. Due to the high pedestrian flow in PTAs, the concentration of large crowds in localized areas results in frequent visual occlusions between pedestrians. Under such conditions, utilizing data from elderly healthcare studies may lead to erroneous estimates of the effectiveness of the proposed models and lower accuracy in real scenarios. Therefore, it is imperative to develop datasets that accurately reflect complex crowds in real scenario dynamics to provide a robust foundation for future research. Additionally, issuing wearable devices to all pedestrians is challenging both economically and policywise. Various smart devices (such as smartphones, smartwatches, smart bracelets, etc.) have become daily necessities, serving as sensor carriers and providing software and hardware support for data analysis and transmission. Furthermore, long-term gait tracking can assess pedestrian fall risk, and focusing on high-risk pedestrians may save public resources and improve management efficiency. In addition, when a pedestrian falls in a PTA, understanding the potential disturbance and its impact on the stability of surrounding people is crucial. It is not only essential to detect or predict the occurrence of falls of a pedestrian but also to continuously assess over a long time series. This continuous assessment should consider factors, such as the duration of the fall, the magnitude of the fall, and the ability of the individual to recover, to determine the level of harm and necessity of intervention. Fall incidents in crowds can induce panic emotion, which propagates as disturbances that compromise overall crowd stability. Elucidating the mechanisms of disturbance propagation and developing effective mitigation strategies within crowds represent critical areas for future research, building on the foundation of fall detection studies.