# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
### Kinematic-Based Methods  
Kinematic characteristics are widely used for fall detection. In studies, fall behavior has been detected by collecting and analyzing kinematic data, such as the impact force and change in the center of mass (CoM). The purpose of the research has been to provide medical assistance for the elderly.  
[PERSON] et al. [8] proposed a novel fall detection model based on a statistical process control chart. The fall indicator in the proposed model was defined using a linear combination of kinematic measures. A trial-and-error method was used to determine the weights of the selected kinematic measures. The results showed that compared to a single kinematic measure, the linear combination of kinematic measures performed better in fall detection. The efficacy of the weights determined through a trial-and-error method in addressing dynamic scenarios warrants further investigation.  
[PERSON] et al. [14] focused on impact mechanics in fall behavior. Instead of using force plates to measure the direct fall impact force, they proposed a generic model for estimating hip-impact forces to assess the severity of sideways falls using kinematic measures. They hired 12 experienced judokas to perform different martial arts on a force plate and collected kinematic data. By analyzing the data, four variables were determined as inputs: maximum upper body deceleration, body mass, shoulder angle at the instant of \"maximum impact\", and maximum hip deceleration. As the experimental data are confined to fall-prone populations, such as the elderly, the proposed model requires further enhancement in terms of generalizability. Future studies should aim to validate its effectiveness across diverse populations and in more complex scenarios.  
[PERSON] et al. [21] conducted an uncontrolled manifold (UCM) analysis to test the effects of fall history on kinematic synergy. Elderly volunteers were divided into two groups as experimental subjects. One group had a fall history within 12 months. Volunteers walked at different speeds on a pathway, and their kinematic data were collected and analyzed using the UCM. The results showed that fall history increased the kinematic synergy. The proposed method does not account for the influence of the upper body on kinematic synergy. Furthermore, this method demands high precision in the information regarding the body structure and kinematic characteristics of the lower body; its efficacy in real-world scenarios necessitates further experimental validation.  
[PERSON] et al. [22] proposed an approach for reorganizing accidental falls based on symmetry. The skeletal information of the human body was extracted using OpenPose. Fall behavior was detected using three key parameters: speed of descent at the center of the hip joint, human body centerline angle with the ground, and the width-to-height ratio of the human body external rectangle. Moreover, they considered the ability of individuals to stand up after falling. The paper has several limitations in the experimental section. Firstly, the model's effectiveness was not validated in complex occlusion scenarios. Secondly, the experimental data used for fall behavior detection were all captured from a lateral perspective, resulting in inadequate sample diversity.  
To investigate how variance in segmental configurations stabilize the CoM related to future falls, [PERSON] et al. [25] collected kinematic data of 30 community-dwelling elderly while walking using a 3D motion capture system. After one year, 12 participants fell. By comparing the differences among the elderly, they found that those who had a fall history showed destabilization of the CoM in the vertical direction. But, the experimental data collection was conducted using questionnaires, which may raise concerns about the reliability and objectivity of the obtained data.