## 2 Literature Review  
In this section, we describe both proposals for monitoring risks in workplaces and studies that discuss the use of workers' sensitive data by this type of solution. Firstly, we present systems that use IoT-based technologies to prevent and/or identify accidents in workplaces, mitigate and/or eliminate risks, check working conditions, and check workers' physiological data. The studies were selected using similarity as a criterion with the proposal of this work regarding technologies and/or monitored quantities. In the study by [13], a continuous monitoring device was proposed to measure the physiological variables of miners at high altitudes, where they must handle extreme climatic and physiological hazards without specialized medical supervision. The system includes the monitoring of physiological variables such as electrocardiogram and respiratory activity and environmental variables. The non-invasive sensors of the proposed system are embedded throughout each worker's T-shirt. The device calculates heart and respiration rates and exchanges data with a central monitoring station. [PERSON] et al. [14] proposed a smart PPE solution with a sensor network located on a helmet and a belt to monitor the state of the worker and the environment. The system implements continuous risk monitoring biometrics of the worker, detects the external impact, shock, luminosity, gases, and temperature, and provides real-time recommendations to workers. The data collected by the system can be visualized by the user on a tablet or a mobile phone. The device incorporates an LED strip on the sides of the helmet that activates automatically if the worker is in poorly lit areas. The study conducted by [PERSON] et al. [15] focused on monitoring the level of physical load during construction tasks to assess the ergonomic risk of an individual construction worker. By using a wearable inertial measurement sensor to monitor a worker's bodily movements, this study investigated the feasibility of identifying various physical loading conditions by analyzing a worker's lower body movements. To develop a method for physical load detection, this study first analyzed the effect of the physical loads on a worker's gait cycle and examined whether physical loads create any identifiable perturbations on the signals collected from an ankle-worn wearable inertial measurement unit. This study used machine learning to detect different physical load levels. [PERSON] et al. [17] proposed a smart helmet prototype that monitored the conditions in the workers' environment and performed a near real-time evaluation of risks. The data collected by the sensors were sent to an AI platform for analysis. The equipment aims to protect the operator from impacts while monitoring light, humidity, temperature, atmospheric pressure, presence of gases, and air quality. Alerts can be transmitted to the operator by sound beeps. An LED strip deployed on the helmet can notify the worker of anomalies in the environment through color codes. [PERSON] and [PERSON] [18] present an IoT-based low-power consumption device to help industries track air quality. The device measures temperature, humidity, levels of carbon dioxide, and PM2.5 and PM10 particles. The NodeMcu ESP8266 acts as a controller and a printed circuit board (PCB) is used to integrate all hardware components of the proposed architecture. The authors related that the proposed system showed the feasibility of creating a network of sensors to monitor air quality in all workstations in a factory environment, especially because of the use of common low-cost hardware. [PERSON] et al. [19] proposed a real-time surveillance helmet for mine workers based on IoT. The proposed prototype is a three-tier architecture. The first one measures the health and environment parameters through Arduino Uno and sensors for temperature and humidity, gas, dust, heartbeat, and fire. The second tier is the fuzzy classifier, which determines the environment types based on the first-tier parameters.