Human performance can significantly affect the success and safety of works. For example, the crew piloting an airplane, surgeons in the surgical room, and controllers managing air traffic. The objective assessment of human performance would therefore allow for better tailor and intervene on the management and ongoing work of the operator to finally maximize their efficiency and mitigate the risk of committing errors. In general, human error is consistently identified as one of the main causes of accidents. In this regards, Human Factors (HFs) research investigates the causes of operators' most common errors with the aim of preventing degradation of performance that could bring the operator out of the safety limits. Innovative approaches based on the use of operator's neurophysiological mental states evaluation have been proposed in addition to conventional methods (questionnaires, self-reports, expert evaluation). Being based on real time data directly linked to mind-body reactions, thus intrinsically objective if compared with self-assessed and subjective evaluation, this approach turned out to effectively and rapidly detect any impairment of performance. In fact, it was demonstrated how user's performance heavily depends on interactions between different HFs (e.g. Stress, Attention). The Human Performance Envelope (HPE) was proposed to completely define single operator's performance along with their HFs interaction. However, HPE is still a psycho-behavioural model that lacks of a reliable measure. To overcome this limitation, NEUROID aims to generate a measurable HPE model by combining machine learning (ML) algorithms and multivariate autoregressive (MVAR) models for the analysis of the operator's neurophysiological signals. More specifically, the HFs time-series will be modelled as a network where the nodes are the HFs, and the edges their directed causal relationships. The characterization of this network will allow to model and quantify the operator's HPE.
NEUROID is characterized by a unique and novel methodological framework and it will undoubtedly have a remarkable immediate impact on domains where neurophysiological operator monitoring is fundamental and directly linked to social, safety, and economic aspects. NEUROID will open new research frontiers for investigating human performance from a completely new perspective since it will effectively combine and measure all the aspects underlying human behaviour and human-machine interaction without omitting any information. In fact, traditional methods (self-reports and interviews) to catch information about the operators' HFs do not allow for quantifying unconscious phenomena and feelings underlying human behaviours, and most importantly they do not provide any measure of such unconscious reactions experienced by operators while performing working activities. Understanding which cognitive and emotional resources are required by the operators while coping with their tasks is crucial for future development of training, intelligent supporting agent (e.g. adaptive automation), safety management system (SMS), specific formation program, and more in general for all those applications in which the human performance is directly linked to the safety. In this regard, NEUROID will generate an innovative and systematic approach to quantify and objectively measure HFs by taking into account, at the same time, the behaviours, emotions, and the mental reactions of the operators themselves, and integrating them with the data related to performance. Although the duration of the NEUROID project is short (12 months) it will have a huge impact on different areas. For example, the results will enlarge existing knowledge about brain and physiological features related to HFs assessment, and will pave the way for an innovative evaluation of Human-Machine Interaction (HMI) and for the definition of guidelines concerning HPE and performance correlates. For example, the real-time and neurophysiological HMI assessment would be very useful along the design of futuristic technology and automations to identify the most appropriate solution able to mitigate error commission and, at the same time, ensure the proper level of safety. Over time, NEUROID technology could also make its entrance in industry with the development of preventive tools to enhance safety and mitigate error commission, improve work effectiveness, and guarantee both operators and users' health and safety. The NEUROID methodology will therefore innovate domains of Human Factors, Computer Science, Safety Management System, and Neuroergonomics as it will open new frontiers for investigating human behaviour in specific user-centred contexts, improving training, and developing specific formation programs. Moreover, the project will likely foster the development of preventive tools to enhance safety and mitigate error commission, improve work effectiveness, and guarantee both operators and users' health and safety. In an even wider perspective, NEUROID methodology will establish new potential "channels of communication" based on online measures of single-member and team performance enabling relevant outcomes for a huge set of European social challenges such as Smart & Connected Environments (i.e. Industry 4.0), Education, Transportation, Health & Safety. Finally, the current progress in terms of wearable sensors will allow for the development of portable monitoring system by which defining a closed-loop between the operator and the machine also under realistic settings, that is making the HPE assessment online and during real working activity and environment.