Based on computer vision techniques, our eye-tracking system integrates eye images and head movement, in real time, performing a robust gaze point tracking. Nystagmus movements due to vestibulo-ocular reflex are monitored and integrated. Our system is robust against changes of illumination conditions and it is equipped with accurate inertial motion unit to detect the head movement enabling eye gaze even in dynamical conditions. HAT-Move performance has been investigated in both static and dynamic conditions, in laboratory settings and in a 3D virtual reality scenario, showing that HAT-Move is able to achieve eye gaze angular error of about 1 degree.
Eye-trackers and pupil size variation are generally developed for scientific investigation in several fields of application such as ophthalmology, neurology, or psychology, with the aim of studying oculomotor characteristics and abnormalities. The focus of these studies is the identification of cognitive and mental states. Our algorithms implemented in efficient software provide useful cues to discriminate emotional states, for example, induced by viewing images at different arousal content.
The electrodermal activity (EDA) is a reliable physiological signal for monitoring the sympathetic nervous system and manifests itself as a change in electrical properties of the skin. Several studies have demonstrated that EDA can be a source of effective markers for the assessment of emotional states in humans. There are two main methods for measuring EDA: even though the DC approach is the most widely used in the commercial devices, the admittance contribution of EDA, estimated through and AC approach, can affect the EDA statistical power in inferring on the subject’s arousing level.
We propose a novel textile wearable system, which is able to perform an EDA measurement using both AC and DC methods (in the range from DC to 1 kHz of AC).
In addition to an efficient hardware, the analysis of EDA signals needs very powerful tools. The novel cvxEDA tool implemented a model that describes EDA as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization and sparsity. The algorithm has been evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the activity of the autonomic nervous system in response to strong affective stimulation. These and other experimental applications in the field of affective computing have shown extremely good performance.
A growing number of institutions and companies use e-learning as a training integrative practice or sometimes even exclusive.
Nowadays, however, there are no systems on the market able to provide a reliable and objective feedback on the effectiveness of the e-learning procedures. Questionnaires of post-course evaluation, in fact, consist of questions whose answers are highly subjective and could be determined by the psychophysiological state of the subject.
Thanks to the solutions offered by Feel-ING you can objectively monitor whether the user has actually paid attention to what is displayed on the PC screen and its level of stress or anxiety.
We offer an important tool for all companies that use e-learning courses (e.g. staff training on topics such as safety or quality), and for evaluating the effectiveness of online college courses.
In addition, we can be able to provide services and information that can be related to the state of physical and / or mental stress, hyper-autonomic activation (e.g. tachycardia for various reasons) by the use of wearable devices for the monitoring of physiological variables. The use of wearable instruments meets market requirements relating to long-term monitoring at home or in any other conditions.