Nowadays, the attention toward the working environment grows sensibly: one person out of ten in Europe is suffering from one or more work-related health problems and each year there are more than 3.000 fatal work-related accidents in Europe. One of the major causes of such accidents is the human error, strongly correlated with the worker's tiredness, stress, and mental workload. The neurophysiological assessment of these human factors is well validated and known in scientific literature but there are still several open issues regarding its spread in a working context. For example, one of the main issue corresponds to the invasiveness associated to the workers' evaluation which could negatively interfere with the worker's activities and therefore it could imply a very low acceptance toward such a kind of evaluation. The present project aims at developing a zero-invasiveness tool to respond to the need for non-contact evaluation of workers.
On one side, a contactless approach for the workers¿ evaluation plays a critical role in specific fields, i.e. the road hauliers, because it could not negatively interfere with the worker¿s tasks. Furthermore, a contactless approach would also not affect the worker's performance. On the other side, a contactless approach would allow the workers evaluation while also respecting the social distancing practices formally provided by the World Health Organization to face the pandemic COVID-19 emergency.
The REmote tool for Mental State evaluation (REMES) project would face a double-open issue in the industrial and scientific communities: (1) improve the wellbeing and safety of the workers in their working place through a neurophysiological evaluation while respecting their privacy; (2) validate a contactless methodology to estimate mental workload, stress, tiredness and emotional state, evaluating different autonomic parameters such as the Heart Rate (HR), the Eye Blinks Rate (EBR) and the Respiration Rate (RR).
The employment of neurophysiological and physiological measurements to evaluate mental states has been largely explored and validated in aviation, maritime domain, general working environments and driving (Babiloni, 2019; Borghini, Di Flumeri, et al., 2020; Di Flumeri et al., 2018a). The benefits of employing such methodologies to monitor mental states for industrial and healthy purpose are scientifically wide accepted. The limitations of such methodologies are mainly represented by the related costs for the equipment and by the sensors¿ invasiveness, which in real working conditions could irreparably interfere with worker's activities.
Therefore, the REMES project would address the aforementioned limitations by proposing a low cost and zero-invasiveness tool. In this context, the camera needed for the facial video recording would be a simple RGB commercial camera (related cost is around 100€) and the Python software code is simple enough to run on a low cost Windows mini-PC (e.g. Raspberry PI Model 4). Two preliminary studies were conducted for validating the non-contact approach to evaluate the HR and the outcomes resulted very promising (Ronca et al., 2021; Ronca et al., 2020). Such a non-contact approach could address also the evaluation of other parameters primary involved in the mental workload and stress evaluation (Band, Borghini, Brookhuis, & Mehler, 2019; Borghini, Di Flumeri, et al., 2020), such as the Heart Rate Variability (HRV) and the Inter-Beat Interval (IBI), by just employing a more capable RGB camera in terms of frame rate.
In addition, the REMES project would propose one of the first tool to evaluate mental states in real time and in real environments fully compatible with the recent World Health Organization provisions centred on social distances practices to face the pandemic COVID-19 (Fong et al., 2020). To summarize, REMES research will produce outcomes for the following relevant aspects:
- It will enlarge the field of application related to the HR, EBR and RR physiological features evaluation to evaluate the worker's mental workload, stress and emotional state;
- It will reduce the privacy issues related to worker¿s evaluation in real time and in real working conditions;
- It will be suitable in all the cases in which the social distancing practice is mandatory.
BIBLIOGRAPHY
Babiloni, F. (2019). Mental Workload Monitoring: New Perspectives from Neuroscience. Communications in Computer and Information Science, 1107, 3¿19. https://doi.org/10.1007/978-3-030-32423-0_1
Band, G. P. H., Borghini, G., Brookhuis, K., & Mehler, B. (2019, November 19). Editorial: Psychophysiological Contributions to Traffic Safety. Frontiers in Human Neuroscience, Vol. 13. https://doi.org/10.3389/fnhum.2019.00410
Borghini, G., Di Flumeri, G., Aricò, P., Sciaraffa, N., Bonelli, S., Ragosta, M., ¿ Babiloni, F. (2020). A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers. Scientific Reports, 10(1), 8600. https://doi.org/10.1038/s41598-020-65610-z
Di Flumeri, G., Borghini, G., Aricò, P., Sciaraffa, N., Lanzi, P., Pozzi, S., ¿ Babiloni, F. (2018). EEG-based mental workload assessment during real driving: A taxonomic tool for neuroergonomics in highly automated environments. In Neuroergonomics: The Brain at Work and in Everyday Life (pp. 121¿126). https://doi.org/10.1016/B978-0-12-811926-6.00020-8
Fong, M. W., Gao, H., Wong, J. Y., Xiao, J., Shiu, E. Y. C., Ryu, S., & Cowling, B. J. (2020). Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings-Social Distancing Measures. Emerging Infectious Diseases, 26(5), 976¿984. https://doi.org/10.3201/eid2605.190995
Rahman, H., Ahmed, M. U., & Begum, S. (2020). Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration. IEEE Transactions on Biomedical Engineering, 67(1), 88¿98. https://doi.org/10.1109/TBME.2019.2908349
Rahman, H., Uddin Ahmed, M., Begum, S., & Funk, P. (n.d.). Real Time Heart Rate Monitoring From Facial RGB Color Video Using Webcam. Retrieved from http://stressmedicin.se/neuro-psykofysilogiska-matsystem/cstress-.
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V. Ronca et al., ¿Contactless Physiological Assessment of Mental Workload During Teleworking-like Task,¿ in Communications in Computer and Information Science, 2020, vol. 1318, pp. 76¿86.