  The last decade's market has been characterized by wearable devices, mainly
smartwatches, edge, and cloud computing. A possible application of these
technologies is to improve the safety of dangerous activities, especially
driving motor vehicles. Common enabling technologies, such as system-on-chip,
ultra-low-power computational platforms, and wide-band wireless connectivity,
push all these trends. On the one hand, wearable devices, thanks to the
continuous contact with the user's body, can measure physiological parameters.
On the other hand, edge computing and machine learning techniques, alongside
cameras, allow the implementation of contactless computer vision systems
capable of providing information about the user's current behavior. Another
trend is the usage of RADARs in automotive applications, both for collision
avoidance and monitoring driver behavior. These technologies can be combined to
develop systems designed to aid the driver. For the sake of this paper, we are
focusing on warning drivers, allowing them to know whenever they are drowsy and
hence risking a sleep onset or are not paying attention to the road. Developing
such systems poses many challenges, such as automatic classification of
physiological signal patterns, facial expression recognition, head movements
and eye gaze detection. These challenges have been individually addressed in
the literature. Anyway, we noticed a need for more description on implementing
data fusion. Two main reasons for adopting the fusion approach are to improve
the quality of the overall representation (increasing accuracy and specificity
against drowsy) and make a more reliable system due to redundancy.
