## 5 Conclusions  
In this paper, the combination of Fuzzy Markup Language on JFML and IoT infrastructure has been integrated in a proposed system and tested to prevent FFH at construction sites. Firstly, the most relevant factors for FFH in a construction environment were identified. Specifically, the correct use of a harness, the altitude, the distance to the edge of the fall, and the wind velocity have all been considered. Secondly, several sensors have been employed to collect information about these factors (anemometer, beacons, altimeter, etc.). In addition, an architecture based on the IoT paradigm has been considered and detailed. A panel of experts were consulted to evaluate the risk situations and to check the proposed system. On the basis of their knowledge, a fuzzy logic system (FLS), where some fuzzy rules based on fuzzy variables have been designed and implemented according to the IEEE std 1855-2016, was to be run with JFML. Four input variables and one output fuzzy variable were described and defined. A total of 11 fuzzy rules were defined. In addition, in order to determine the advantages of the proposed system, four different scenarios based on habitual working situations with a risk of FFH were tested and evaluated by a panel of experts. In the first scenario proposed, the worker could fall to an underground level. In the second scenario, the worker was assembling formworks in a building structure. In the third scenario, the worker was developing tasks on a temporary scaffold. In the last one, the case of a worker on a roof was considered. It is remarkable that at least one rule defined in the FLS and related to the harness detection was fired in the four scenario simulated. As a consequence, the management of the appropriate use of the harness in dangerous zones is a key aspect to prevent FFH. This conclusion is aligned with previous studies focused on the prevention of FFH in construction sector. The results obtained are relevant in the field of safety at work and are in accordance with the panel of experts. The proposed system is flexible and scalable. The system could be expanded with the integration of new specific sensors in order to obtain information on other occupational risks in construction projects. For example, ultra-wideband sensors could provide the distance between the worker and heavy equipment. The extension of the system would not be complex. New sensors can be connected to the broker. Tentative new rules would be defined, and after approval by the expert panel, they can be integrated into the FLS. It should be noted that the IOT-JFML system was performed for only one construction worker, although its applicability can be extended to a greater number of construction workers. As a consequence in future research, it could applied to a group of workers in different workplace scenarios. The information provided can help to coordinate construction safety practices. The key advantage of the proposal is its real-time capacity to evaluate the levels of risk to the construction workers, according to the values obtained by the sensors. It is an important advance when compared with traditional risk assessment, where the majority of risk is evaluated before the beginning of the construction project; however, the FFH risk level can change in dynamic environments such as a construction site. Then, the  
Figure 12: Risk levels estimated by the system for Scenario 4 (roofing tasks). continuous monitoring of the workers in a wireless way, considering the main variables of FFH, provides the option to update construction risk levels in real time. The proposed system could improve the risk perception by workers and managers because risk levels are based on real data, in contrast to traditional methods, which are based on estimated data obtained from formal construction projects and not from real construction sites. Work instructions from safety coordinators based on real time data supplied by the IOT-JFML proposal will improve construction site safety levels.