  Medical applications have benefited greatly from the rapid advancement in
computer vision. Considering patient monitoring in particular, in-bed human
posture estimation offers important health-related metrics with potential value
in medical condition assessments. Despite great progress in this domain, it
remains challenging due to substantial ambiguity during occlusions, and the
lack of large corpora of manually labeled data for model training, particularly
with domains such as thermal infrared imaging which are privacy-preserving, and
thus of great interest. Motivated by the effectiveness of self-supervised
methods in learning features directly from data, we propose a multi-modal
conditional variational autoencoder (MC-VAE) capable of reconstructing features
from missing modalities seen during training. This approach is used with HRNet
to enable single modality inference for in-bed pose estimation. Through
extensive evaluations, we demonstrate that body positions can be effectively
recognized from the available modality, achieving on par results with baseline
models that are highly dependent on having access to multiple modes at
inference time. The proposed framework supports future research towards
self-supervised learning that generates a robust model from a single source,
and expects it to generalize over many unknown distributions in clinical
environments.
