Wearable sleep-tracking technology is of growing use in the sleep and circadian fields,
including for applications across other disciplines, inclusive of a variety of disease states …

Millions of wearable-device users record their heart rate (HR) and activity. We introduce a
statistical method to extract and track six key physiological parameters from these data …

Classifying sleep stages in real-time represents considerable potential, for instance in
enabling interactive noise masking in noisy environments when persons are in a state of …

The previous American Academy of Sleep Medicine (AASM) Consumer and Clinical
Technology Committee and the subsequent Technology Innovation Committee have …

Although the importance of sleep is increasingly recognized, the lack of general and
transferable algorithms hinders scalable sleep assessment in healthy persons and those …

In the past few decades, mathematical models based on dynamical systems theory have
provided new insight into diverse biological systems. In this review, we ask whether the …

Inertial Measurement Units (IMUs) within an everyday consumer smartwatch offer a
convenient and low-cost method to monitor the natural behaviour of hospital patients …

Objective. To develop a sleep staging method from wrist-worn accelerometry and the
photoplethysmogram (PPG) by leveraging transfer learning from a large electrocardiogram …

The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of
sleep in large populations. Personalized yet device-agnostic algorithms can sidestep …

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's
disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to …