## 2 Background
### FFH Management on Construction Site  
Management of a workers' personal fall arrest system (PFAS) is not easy because not all PFAS are compulsory at every location on a construction site. In general, the use of PFAS is regulated by national safety rules, more specifically by safety plans for each construction project. According to the tasks and the risk level of the workplace zone, a PFAS could be compulsory but may not be. For instance, it is not mandatory to connect the harness to the lifeline while workers are working at ground level without risk of falling, but it should be connected to the lifeline once the worker comes into a risky zone, such as a scaffold, a roof, or similar. Sometimes fall arrest systems are not available in dangerous zones; however, a common circumstance is that harnesses can be available at the workplace but they are not properly used [8]. Workers' use or non-use of PFAS is not a random action, because the decision is influenced by different variables [38]. Some authors found that gender, contract forms, and safety training all affected the appropriate use of PPE by construction workers [39]. Similarly, it was found that the influence of organizational factors was important, such as the availability of PPE training on the use of PPE devices, or safety policies regarding PPE, as well as other individual variables, such as knowledge of PPE and personal perception towards the use of PPE [40]. In addition, safety management practices, such as safety supervision, were considered a crucial factor that might influence the use or non-use of PPE [38]. In the particular case of falls, the idea of a fall detection system is not exclusive to workers. A robust fall detection system can contribute in time to addressing a fall in the case of an elderly adult falling at home as well as a worker at work. In any case, IoT solutions to monitoring falls can be classified according to the type of technology used (Table 2). \begin{table}
\begin{tabular}{l l l l} \hline
**Technology** & **Authors** & **Proposal** & **Signal** \\ \hline \hline \multirow{2}{*}{Wearable sensor} & [19] & Monitoring use of Harness by workers & RSSI \\ \hline \hline Convolutional & [42] & Detect safety harness wearing & Image \\ \hline \hline \multirow{2}{*}{Smartphone sensor} & [43] & An algorithm to detect falls based on the Euler angle and acceleration & Acceleration \\ \hline \hline \multirow{2}{*}{RGB/IP Image Sensor} & [44] & Analyzing human shape deformation during a video sequence & Image \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Available technologies for fall detection. Adapted from [PERSON] et al. [41]. Each existing system presents advantages, disadvantages, and limitations. For instance, the detection of workers with harnesses based on a convolutional neural network [42] implies a complex installation of the the system, and its use is limited to a reduced number of tasks at height. Similar systems based on image processing [44; 45] are limited by the installation of the cameras and configuration of the system in a specific area. These limitations reduce their applicability in dynamic environments, such as in construction sites. Other proposals based on acoustic signals can be very useful at low noise environments [50], but their robustness can be seriously reduced in a noisy environment, such as construction projects. Similarly, the accuracy and robustness of fall detection systems based on Eco [47] and ultrasound systems [48] can be reduced by the presence of unexpected obstacles, and materials obstructing the signal. In addition, the cost of communication infrastructure in a temporary workplace can be particularly high. In contrast, other sensor technologies, such as BLE [51] and RFID [52] are more suitable for applications in construction. The main advantages of these technologies are their easy configuration, good maintainability, accuracy, and stability, and their low power usage [53]. Furthermore, they present high possibilities for integration with other devices [51; 54].