  Sleep posture is linked to several health conditions such as nocturnal cramps
and more serious musculoskeletal issues. However, in-clinic sleep assessments
are often limited to vital signs (e.g. brain waves). Wearable sensors with
embedded inertial measurement units have been used for sleep posture
classification; nonetheless, previous works consider only few (commonly four)
postures, which are inadequate for advanced clinical assessments. Moreover,
posture learning algorithms typically require longitudinal data collection to
function reliably, and often operate on raw inertial sensor readings unfamiliar
to clinicians. This paper proposes a new framework for sleep posture
classification based on a minimal set of joint angle measurements. The proposed
framework is validated on a rich set of twelve postures in two experimental
pipelines: computer animation to obtain synthetic postural data, and human
participant pilot study using custom-made miniature wearable sensors. Through
fusing raw geo-inertial sensor measurements to compute a filtered estimate of
relative segment orientations across the wrist and ankle joints, the body
posture can be characterised in a way comprehensible to medical experts. The
proposed sleep posture learning framework offers plug-and-play posture
classification by capitalising on a novel kinematic data augmentation method
that requires only one training example per posture. Additionally, a new metric
together with data visualisations are employed to extract meaningful insights
from the postures dataset, demonstrate the added value of the data augmentation
method, and explain the classification performance. The proposed framework
attained promising overall accuracy as high as 100% on synthetic data and 92.7%
on real data, on par with state of the art data-hungry algorithms available in
the literature.
