A noise-robust Koopman spectral analysis of an intermittent dynamics method for complex systems: a case study in pathophysiological processes of obstructive sleep apnea

Navigating the night: evaluating and accessing wearable sleep trackers for clinical use

“…These AI-based algorithms leverage diverse features, including movement patterns, physiological signals, and environmental factors to analyze and score sleep, offering a more holistic understanding of sleep conditions and leading to new standards for evaluating wearable devices. Moreover, the edge computing technology processes and analyzes data directly on the device [ 26 ], enabling real-time insights into various clinical conditions of cardiac, neurodegenerative, and respiratory diseases [ 27 , 28 ]. Utilizing such edge computing systems for sleep health monitoring results in faster, more efficient data processing, decreasing latency, and lessening dependence on cloud connectivity.…”