# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 4 Results
### A Statistical Analysis of Publications
#### 4.2.2 Real-Time Monitoring Systems
[63] employed the use of head-mounted cameras to capture images from a first-person perspective (FPV) for the purpose of monitoring workers. The utilisation of three-dimensional point clouds enables the identification of potential hazard locations in real time, the documentation of worker behaviours, and the monitoring of near misses. The acquisition of this information has enabled the implementation of more effective training programmes and an enhancement of workplace safety. * Wearable technology  
The term \"wearable technology\" is used to describe clothing and personal accessories that incorporate advanced computer and electronic technologies. Wearable technology is employed in a variety of applications, including wearable cameras, smart clothing, activity trackers, smartwatches, and others. Furthermore, the technology offers numerous advantages in the domain of occupational safety. [PERSON] et al. [34] implemented the Worker Trajectory Analysis System (WoTAS), which employs Bluetooth Low Energy (BLE) beacons to track worker trajectories, thereby enabling the real-time collection and analysis of data on worker movements. The Semantic Trajectories for Dynamic Environments (STriDE) ontology model, in conjunction with a Hidden Markov Model (HMM), facilitated the identification of potentially hazardous areas and events. Similarly, [PERSON] et al. [19] employed the use of smartphones with integrated sensors to record the kinetic changes associated with the movement of workers. The data were subjected to analysis using an artificial neural network (ANN) in order to identify situations that were conducive to falls. [PERSON] et al. [64] employed the use of smart wearable devices, such as smartphones equipped with a three-axis accelerometer, to monitor the movements of workers on a simulated construction site. The data collected by the devices were processed by an ANN, which enabled the identification of near misses and the exact time of their occurrence. Furthermore, [PERSON] et al. [17] employed IMUs to gather data on workers' movements and attitudes, thereby enabling the precise monitoring of typical activities and the identification of near misses. * Motion sensors  
[PERSON] et al. [65] employed the use of motion sensors in the form of IMUs to facilitate the automatic detection of near-miss events in metallurgical movements. This was achieved through the training of a semi-supervised algorithm based on a Laplacian support vector machine. The algorithm demonstrated a classification accuracy of over 98%. Similarly, [PERSON] et al. [66] employed analogous IMU sensors attached to the sacrum of workers to gather kinematic data while traversing a steel structure. The determination of threshold values was achieved through the utilisation of features such as the sum vector magnitude and the normalised signal magnitude area (SMA), as evidenced by the experimental findings. [PERSON] et al. [67] also employed wearable inertial measurement units (WI-MUs) to collect kinematic data, which was then analysed using a single-class support vector machine. This method allowed for the automatic detection of potential accident eventssuch as slips and trips. This approach enabled the identification of at-risk workers without disrupting their work activities.