# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
### Smartphone-Based Methods  
Smartphones have been widely used over the past decade, and most people carry smartphones during their daily activities. Most smartphones provide various data using embedded sensors. Therefore, fall detection using smartphones has been focused on and studied. [PERSON] et al. [9] designed a series of experiments to determine the sensitivity and specificity of smartphone types and placements for smartphone-based fall detection. Eight volunteers participated in the simulated fall experiments, each carrying two mobile phones in different positions. Ten types of true falls, five types of falls with recovery, and eleven daily activities were simulated. The experimental results showed that the types and locations of the mobile phones affected the fall detection accuracy. The sensitivity of smartphone sensors leads to data noise issues during the acquisition of acceleration data, which may affect the accuracy of fall detection. Considering that employing a smartphone as a unique sensor in a fall detection system may be accompanied by several limitations, [PERSON] et al. [11] proposed a smartphone-based fall detection system incorporating a set of small sensing modes. The experimental results showed that the position of the smartphone did not increase the efficiency of the system; therefore, it could only be used to process signals or alerts from the system. Thus, a user can pay less attention to the position of the smartphone, and it can even be transported to an external point close to the user. During experiment data collection, the paper proposes various activities of daily living as a control group based on user characteristics, rather than scenario features. To improve the safety of smartphone users and prevent them from falling, [PERSON] et al. [13] proposed a ground-changing detection system called InfraSee, which is based on mobile phone infrared sensors. In this system, to reduce the energy consumption of the system, the infrared sensor can be turned off in specific situations. In addition, if a danger is detected ahead, it determines whether to issue an alert according to the reaction of the user. If a user is already aware of a danger, the system will not issue an alert. The proposed system not only requires a smartphone but also necessitates the addition of an infrared sensor,imposing an extra usage burden on pedestrians. Furthermore, the system experiences numerous false alarm issues when deployed in crowded areas. [PERSON] et al. [15] proposed a fall detection algorithm based on a threshold to solve the problem that fall detection based only on acceleration is prone to false alarms. In this study, built-in inertial measurement unit sensors of a smartphone were utilized to detect human falls, and an SVM was used to classify activities of daily living (ADL). Eight volunteers carrying smartphones on their bodies participated in the experiment to complete four different types of falls. The analyses of the experimental results showed high accuracy. However, in fact, smartphone positions often are changed according to the varying application situation. Therefore, it is difficult to maintain the fixed smartphone positions. Impaired postural stability is an important predictor of falls in elderly individuals. [PERSON] et al. [16] determined whether a smartphone-embedded accelerometer can measure postural stability. In the experiments, 30 elderly people participated in a balance test, and the data were collected by holding a smartphone and a force plate, respectively. The results showed a moderate-to-high significant correlation between measurements from the force plate and the smartphone, indicating that the smartphone is a valid measurement tool for postural stability. The paper confirms that smartphones can measure postural stability, but there is a lack of thorough analysis of the relationship between postural stability and fall detection. [PERSON] et al. [24] considered that the clinical assessment of falls is expensive and requires non-portable equipment and specialist expertise.