  Although the field of sleep study has greatly developed over the recent
years, the most common and efficient way to detect sleep issues remains a sleep
examination performed in a sleep laboratory, in a procedure called
Polysomnography (PSG). This examination measures several vital signals during a
full night's sleep using multiple sensors connected to the patient's body. Yet,
despite being the golden standard, the connection of the sensors and the
unfamiliar environment inevitably impact the quality of the patient's sleep and
the examination itself. Therefore, with the novel development of more accurate
and affordable 3D sensing devices, new approaches for non-contact sleep study
emerged. These methods utilize different techniques with the purpose to extract
the same sleep parameters, but remotely, eliminating the need of any physical
connections to the patient's body. However, in order to enable reliable remote
extraction, these methods require accurate identification of the basic Region
of Interest (ROI) i.e. the chest area of the patient, a task that is currently
holding back the development process, as it is performed manually for each
patient. In this study, we propose an automatic chest area segmentation
algorithm, that given an input set of 3D frames of a sleeping patient, outputs
a segmentation image with the pixels that correspond to the chest area, and can
then be used as an input to subsequent sleep analysis algorithms. Except for
significantly speeding up the development process of the non-contact methods,
accurate automatic segmentation can also enable a more precise feature
extraction and it is shown it is already improving sensitivity of prior
solutions on average 46.9% better compared to manual ROI selection. All
mentioned will place the extraction algorithms of the non-contact methods as a
leading candidate to replace the existing traditional methods used today.
