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Sleep posture is linked to several health conditions such as nocturnal crampsand more serious musculoskeletal issues. However, in-clinic sleep assessmentsare often limited to vital signs (e.g. brain waves). Wearable sensors withembedded inertial measurement units have been used for sleep postureclassification; 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 tofunction reliably, and often operate on raw inertial sensor readings unfamiliarto clinicians. This paper proposes a new framework for sleep postureclassification based on a minimal set of joint angle measurements. The proposedframework is validated on a rich set of twelve postures in two experimentalpipelines: computer animation to obtain synthetic postural data, and humanparticipant pilot study using custom-made miniature wearable sensors. Throughfusing raw geo-inertial sensor measurements to compute a filtered estimate ofrelative segment orientations across the wrist and ankle joints, the bodyposture can be characterised in a way comprehensible to medical experts. Theproposed sleep posture learning framework offers plug-and-play postureclassification by capitalising on a novel kinematic data augmentation methodthat requires only one training example per posture. Additionally, a new metrictogether with data visualisations are employed to extract meaningful insightsfrom the postures dataset, demonstrate the added value of the data augmentationmethod, and explain the classification performance. The proposed frameworkattained 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 inthe literature.

Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies