# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
### Wearable Device-Based Methods  
A wearable device can be placed in helmets, clothing, belts, shoes, and other areas on the human body, and it can provide various data for fall detection research. Most research on fall behavior detection based on wearable devices aims to provide efficient and reliable medical assistance to the elderly. The main research content can be categorized into three issues: accuracy of fall behavior detection, energy endurance of wearable devices, and acceptance of wearable devices by the elderly. [PERSON] et al. [19] proposed a wearable sensor-based continuous fall monitoring system to detect fall behavior and identify fall patterns. They also analyzed the individual performance of an accelerometer and gyroscope for fall detection. The performance of the proposed system was further investigated through a series of experiments using three machine learning algorithms. The study shows that the fusion of sensor data may enhance the accuracy of fall detection. However, the persuasiveness of this finding is somewhat limited due to the inclusion of experiments combining only two types of sensor data. [PERSON] et al. [20] proposed a novel fall detection system using wearable sensors that exploited the covariance matrix as a feature extractor and fusion approach of raw signals. The effectiveness of the covariance matrix in enhancing the classification performance was demonstrated by testing two publicly available fall datasets. Although the proposed system exhibits enhancements in the experiment results compared to other similar methods, it still remains at the conceptual level. [PERSON] et al. [27] pointed out that in many fall detection studies, volunteers are instructed to simulate falling behavior in a controlled laboratory environment and use these data to validate the effectiveness of the detection method. To evaluate the adequacy of this method, the statistical characteristics of the acceleration signals from two real fall databases and simulated falls from well-known related works were compared. The results of the comparison showed noteworthy differences between real-life and simulated falls, which indicated the necessity to alter the strategies for evaluating wearable fall detectors. [PERSON] et al. [31] presented a new testing method for fall arrest equipment aiming at protecting workers working at heights. They used a test stand consisting of a Hybrid III 50 th Pedestrian ATD anthropomorphic manikin and a measuring set with three-axis acceleration transducers. The appropriate alarm threshold was determined by detecting falling behavior and falling acceleration in different situations. This testing methodology could potentially complement other accelerometer threshold-based fall detection methods in the future, offering a practical approach to threshold determination. In addition, some studies have used deep learning methods to conduct a more intensive analysis of the data obtained by wearable devices. [PERSON] et al. [28] aimed to enhance the explainability of an existing fall detection model. They proposed a novel variation of a deep learning model that integrated a hierarchical attention mechanism into an existing CNN. This model identified the part of the sensor data that contributed the most to the decision. It was evaluated using two large publicly available datasets. Although cross-validation has been conducted on two datasets to demonstrate the effectiveness and practicality of the proposed model, the performance of the model may still be limited by the datasets. When applied in real-world scenarios, updating model parameters through learning user movement patterns could potentially yield better results. [PERSON] et al. [34] presented a tiny CNN (TinyCNN) with two-stage efficient feature extraction and evaluated it on two large-scale public fall datasets (KFall and SisFall) collected from wearable inertial sensors. The results revealed the black box of TinyCNN and showed most of the model predictions.