  Sleep posture analysis is widely used for clinical patient monitoring and
sleep studies. Earlier research has revealed that sleep posture highly
influences symptoms of diseases such as apnea and pressure ulcers. In this
study, we propose a robust deep learning model capable of accurately detecting
subjects and their sleeping postures using the publicly available data acquired
from a commercial pressure mapping system. A combination of loss functions is
used to discriminate subjects and their sleeping postures simultaneously. The
experimental results show that our proposed method can identify the patients
and their in-bed posture with almost no errors in a 10-fold cross-validation
scheme. Furthermore, we show that our network achieves an average accuracy of
up to 99% when faced with new subjects in a leave-one-subject-out validation
procedure on the three most common sleeping posture categories. We demonstrate
the effects of the combined cost function over its parameter and show that
learning both tasks simultaneously improves performance significantly. Finally,
we evaluate our proposed pipeline by testing it over augmented images of our
dataset. The proposed algorithm can ultimately be used in clinical and smart
home environments as a complementary tool with other available automated
patient monitoring systems.
