IoT and Edge Computing as Enabling Technologies of Human Factors Monitoring in CBRN Environment

Related Works

Wearable devices well designed to collect the user’s behavioral and health data, can provide information about first responder’s health status and tracking (Ometov, 2021). It is possible to employ a wearable system of monitoring with edge and cloud computing, fully integrated. Research studies have focused on transdisciplinary empirical methods on wearable health technologies for health-related purposes, with technological inputs and health output factors (Caining, 2020). Future research on HF smart monitoring related to physio-psychological stress and stress disorders needs transdisciplinary approach, not only based on clinical/health outcomes, but also related to human–computer interaction, medico-legal, ICT and innovation, privacy and security policy issues, models of service delivery (Hilty, 2021). In particular, wearable technologies are investigated as an innovative tool of risk management in construction industry, one of a sectors where human errors and accidents causing injuries more frequently. Literature shows some focus on different types of wearable technologies for prediction of incidents’s likelihood and protection of working people from any injuries, with the discussion of their applicability (AL-sahar, In press). However, research has been conducted in healthcare sector addressing privacy and information security issues that data are exposed or how these data are protected once collected. Some papers show general lack of awareness among wearable’s users about the privacy and security risks (Cilliers, 2020). Recently, due to the advent of big data from wearables for healthcare applications and decision making, many works started applying data mining and knowledge extraction techniques to predict behavioral and health (Kyriakou, 2019; Syed, 2019). Studies about health conditions awareness using wearable sensor connected to Internet of things (IoT) are based on big data analysis, Artificial intelligence (AI) with the use of deep learning mechanism Boltzmann belief network (Muthu, 2020).