Head and Body Pose Classification for Understanding Sleep Behaviour in People Living with Dementia using Video and a Novel Multi-Head Attention-Driven Deep Learning Architecture

Sleep posture is known to be relevant to various sleep disorders, such as sleep apnea, but is not often quantified in sleep monitoring systems. We address this with a novel vision-based approach, which is robust to the challenging conditions (variable lighting, partial occlusions, variable geometry) of inbed monitoring. This paper proposes a novel, attention-driven deep learning framework for the robust classification of head and body pose from infrared (IR) video streams during sleep of older people and people living with Alzheimers. Our architecture integrates a pre-trained convolutional backbone with a novel Multi-Head Channel-Spatial Attention (MH-CSA) module. The MH-CSA mechanism hierarchically identifies salient features by first capturing multi-scale spatial context using parallel heads with varied dilation rates, and then adaptively recalibrating feature importance via integrated Squeeze-and-Excitation blocks. To specifically address class imbalance, the model is optimized using a Dynamic Class-Balanced Focal Loss, which forces the network to focus on hard-to-classify examples from underrepresented classes. Whilst most prior sleep analysis work is developed using data from healthy younger participants, our system was developed and validated on a nocturnal sleep dataset of older adults and people living with Alzheimers disease, with IR video synchronized to clinical video-Polysomnography (vPSG). For head position classification, the system achieved an F1-score of 91% for older adults and 90% for people living with Alzheimers; for body pose prediction, the scores were 91% and 89% for the respective cohorts. These results demonstrate significant potential for application in understanding sleep behavior and informing appropriate sleep interventions.