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- Real-time blink detection as an indicator of computer vision syndrome in real-life settings: an exploratory studyPublication . Lapa, Inês; Ferreira, Simão; Mateus, Catarina; Rocha, Nuno; Rodrigues, MatildeWith the increase in the number of people using digital devices, complaints about eye and vision problems have been increasing, making the problem of computer vision syndrome (CVS) more serious. Accompanying the increase in CVS in occupational settings, new and unobstructive solutions to assess the risk of this syndrome are of paramount importance. This study aims, through an exploratory approach, to determine if blinking data, collected using a computer webcam, can be used as a reliable indicator for predicting CVS on a real-time basis, considering real-life settings. A total of 13 students participated in the data collection. A software that collected and recorded users’ physiological data through the computer’s camera was installed on the participants’ computers. The CVS-Q was applied to determine the subjects with CVS and its severity. The results showed a decrease in the blinking rate to about 9 to 17 per minute, and for each additional blink the CVS score lowered by 1.26. These data suggest that the decrease in blinking rate was directly associated with CVS. These results are important for allowing the development of a CVS real-time detection algorithm and a related recommendation system that provides interventions to promote health, well-being, and improved performance.
- Challenges of learning human digital twin: case study of mental wellbeing: Using sensor data and machine learning to create HDTPublication . Vildjiounaite, Elena; Kallio, Johanna; Kantorovitch, Julia; Kinnula, Atte; Ferreira, Simão; Rodrigues, Matilde; Rocha, NunoHuman Digital Twin (HDT) is a powerful tool to create a virtual replica of a human, to be used for example for designing interactions with physical systems, preventing cognitive overload, managing human capital, and maintaining a healthy and motivated workforce. Building human twins is a challenging task due to the need to reliably represent each corresponding human being, and the fact that human beings notably differ from each other. Therefore, relying solely on expert knowledge is insufficient, and human twins must learn the specifics of each individual in order to accurately represent them. This paper focuses on AI methods for modelling the mental wellbeing of knowledge workers because the mounting cognitive demands of both white-collar and blue-collar work lead to employees’ stress, and stress leads to diminished creativity and motivation, increased sick leaves, and in severe cases, accidents, burnouts, and disabilities. This paper describes the main building blocks of AI-based detectors of mental stress and highlights the main challenges and future directions of research., which are expected to be relevant also for HDT learning in other domains because the high degree of individuality is ubiquitous in all human activities.
- Validation of a video-based system to determine heart rate for stress monitoringPublication . Ferreira, Simão; Rodrigues, Matilde; Rocha, NunoStudies estimate that about 50% of all lost workdays are related to occupational stress. Academic researchers have been using heart rate variability (HRV) as an indicator of stress. As a way of providing the needed heart rate data, an unobtrusive approach points to video plethysmography, being a recent method that needs further investigation and validation. Specific barriers such as room lighting conditions and face movement have been identified as the main risks for software progression. The present chapter presents a validation protocol of a video-based system to determine heart rate for stress monitoring, under different illuminance levels and position conditions. We present an in-depth protocol on how to assess the reliability of a video facial recognition software on collecting physiological data (heart rate), and our software results when compared to the gold standard, Electrocardiogram (ECG).