A new technique to detect eye blinks is proposed based on automatic tracking of facial landmarks to localise the eyes and eyelid contours. Automatic facial landmarks detectors are trained on an in-the-wild dataset and shows an outstanding robustness to varying lighting conditions, facial expressions, and head orientation. The proposed technique estimates the facial landmark positions and extracts the vertical distance between eyelids for each video frame. Next, a Savitzky–Golay (SG) filter is employed to smooth the obtained signal while keeping the peak information to detect eye blinks. Finally, eye blinks are detected as sharp peaks and a finite state machine is used to check for false blink and true blink cases based on their duration. The efficiency of the proposed technique is shown to outperform the state-of-the-art methods on three standard datasets.

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 Alzheimer’s. 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 Alzheimer’s 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 Alzheimer’s; 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.