# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 4 Results
### Methods Used to Analyse the Information and Figures That Have Been Obtained
#### 4.3.2 Analysis Using Artificial Intelligence (AI)
[79] integrated artificial intelligence with Internet of Things (IoT) technology, creating a real-time monitoring system that identifies potential hazards. [PERSON] et al. [61] employed the YOLOv5 deep learning model to monitor the utilisation of protective helmets by construction workers, thereby enhancing safety through the automation of personal protective equipment checks. [PERSON] et al. [80] employed sophisticated image processing techniques, including three-dimensional pose estimation, for the contactless detection of worker falls. The proposed solution enables the early identification of near misses on construction sites. [PERSON] et al. [81] applied Convolutional Neural Networks (CNNs) in conjunction with sound recognition techniques to identify hazards related to impacts from construction equipment. [PERSON] et al. [18] and [PERSON] et al. [82] employed machine learning techniques to analyse data pertaining to near misses associated with tower cranes, as well as to develop advanced time series modelling. In a recent study, [PERSON] and [PERSON] [62] designed a video surveillance system that utilizes deep learning technology to automatically detect and analyse the causes of near misses. [PERSON] et al. [83] applied machine learning (ML) to analyse safety reports, demonstrating how n-gram modelling techniques and sensitivity analysis can enhance decision-making processes on construction sites. [PERSON] et al. [84] utilized Convolutional Neural Networks (CNNs) for the classification of safety records, supporting the identification and management of potential hazards. [PERSON] et al. [63] employed image processing algorithms and machine learning in the Bag of Words (BoW) localisation method for the analysis and classification of visual data. The AI system was able to assist in the accurate estimation of distances from potential hazards and the identification and recording of unsafe behaviours among workers. [PERSON] et al. [67] developed a method using one-class support vector machines (OCSVM) to analyse data from Wireless Inertial Measurement Units (WIMUs). Their AI algorithm enables the identification of near misses, such as falls. [PERSON] et al. [19] applied an ANN to analyse data from motion sensors in smartphones, which monitor changes in energy during worker movement. Their system enables real-time detection of various types of potentially hazardous falls and allows for proactive warnings to workers about dangers, thereby minimizing the risk of actual accidents on construction sites. [PERSON] et al. [85] utilized artificial intelligence in the form of Long Short-Term Memory (LSTM) networks to forecast the movements of workers and construction equipment,analysing previously observed trajectories. Their model incorporated contextual cues such as object placements and type attributes, enabling effective short-term forecasting. [PERSON] and [PERSON] (2018) utilized polynomial regression and Hidden Markov Models (HMMs) to forecast the trajectories of worker movements. These advanced artificial intelligence techniques enabled early detection of near misses, thereby enhancing safety on construction sites.