# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
### IoT-Based Methods  
Fall detection systems based on the IoT are typically combined with sensors or CV. Most IoT-based methods for fall detection are considered applicable to the health of the elderly. Using an embedded fall detection module, an IoT system can provide more efficient assistance to the elderly. The basic structure of a fall detection system based on the IoT is shown in Figure 3.  
[PERSON] et al. [10] proposed an IoT-based wearable system that could mitigate the serious consequences of fall behavior. They minimized the energy consumption of the wearable sensor node in the IoT-based fall detection system and presented the design of an energy-efficient sensor node based on a customized nRF module. The sensor node was inexpensive, lightweight, and adaptable, making the system more efficient and feasible; however, limitations in terms of lifespan, interruptions, and system complexity still exist. [PERSON] et al. [12] proposed an IoT-based information system for elderly health problems that used a three-axial accelerometer and magnetometer, pedestrian dead reckoning, thresholding, and a decision tree algorithm. This system can position a monitored person and detect various behaviors, including falls. The detected behaviors can be classified as normal, suspicious, and dangerous. This system can only provide a coarse classification based on a predefined threshold and is unable to accurately identify specific behaviors. [PERSON] et al. [3] proposed a combination of real-time and future fall prediction and prevention algorithms using the computational capabilities of IoT nodes. This framework provides real-time emergency notifications when fall behavior is detected and provides a mid-term analysis of the monitoring content for a period. The results and data can also be applied by clinical experts for long-term analysis, which can help them estimate the risk of future falls. The data analysis of three time dimensions reduces the false detection rate of fall behavior and improves the efficiency of helping the elderly. While  
Figure 3: Attitude angles of the inertial device. the paper addresses fall behavior prevention, it lacks empirical evidence to quantify the effectiveness of the prevention measures. [PERSON] et al. [18] defined two types of falls based on an analysis of the major fall parameters. Based on the acceleration parameter characteristics of the two fall types, fall behavior test events were generated using an IoT test event generator tool. Moreover, the obtained data were used to define the event processing language of the EspeTech pattern to detect fall behavior. The validation of the proposed IoT system was conducted using generated test events, lacking configuration and testing in real-world scenarios. Furthermore, the classification of fall behavior types appears to be somewhat narrow. To enhance data processing and prediction in the IoT-based health paradigm, [PERSON] et al. [26] proposed an artificial intelligence-based deep CNN to further analyze the causes of fall behavior. The foundational IoT framework was constructed using a Hadoop distributed file system (HDFS) module. The results of simulated experiments on benchmark datasets showed that the improved IoT system had a higher accuracy in the detection and classification of fall behavior. However, based on the experimental results presented in the paper, there is insufficient evidence to support the improvement in fall behavior prediction. [PERSON] et al. [33] proposed a novel energy-aware IoT-based architecture for fall detection and message queuing telemetry transport-based gatewayless monitoring. Based on this novel architecture, a hybrid double-prediction technique based on supervised dictionary learning was proposed to reinforce detection efficiency and increase the reliability of wearable devices. To validate the technique, an offline-controlled dataset was collected for training, and real fall data were used for online testing.