Summary: UCLA researchers from the Department of Medicine-Pulmonary Disease have developed a novel wearable device to detect alertness and predict the onset of sleep for improved safety.
Background:
Hypersomnia and excessive daytime sleepiness (EDS) are both symptoms of a broad class of sleeping disorders including obstructive sleep apnea (OSA), circadian rhythm disturbances, and narcolepsy. These conditions can impair personal health and quality of life and pose public safety risks, particularly in situations where sustained vigilance is necessary. Several pharmacological and behavioral interventions have been developed, but real-time methods of detecting and mitigating changes in alertness remain limited. Existing technologies are either therapeutic or diagnostic and tend to be bulky and non-wearable. Other systems monitor a single physiological marker (e.g., heart rate) and as such lack the multimodal sensing and signal analysis required to detect the nuanced changes associated with drowsiness. Additionally, these technologies do not provide active countermeasures in real-time to mitigate undesired changes in alertness. There remains an unmet need for a wearable device that monitors and predicts changes in alertness and provides cues that can prevent unwanted sleepiness in a wide array of fields.
Innovation: UCLA researchers from the Department of Medicine-pulmonary disease have a developed a novel lightweight and wearable Alertness-Detecting Headset (ADH) that measures physiological signals from the head and face to detect and predict the onset of sleep. The headset integrates several sensors that monitor heart rate, changes in blood pressure, facial muscle tone and eye movement, and head orientation. These signals are processed in real-time and analyzed through artificial intelligence (AI) and machine learning (ML) algorithms that are trained to detect drowsiness and predict sleep-onset. This novel device can additionally provide bone-conducting audio alerts and visual cues with a mobile device to rouse the user. This innovation can revolutionize cognitive health tracking by providing an integrated and scalable platform for neurophysiological function monitoring.
Potential Applications: • Drowsiness detection • Wearable EEG screening • Real-time tracking of sleep cycles • Facial muscle recovery monitoring • Non-invasive tracking • Transportation alertness (trucking, aviation, etc.)
Advantages: • Multimodal physiological monitoring • Predictive alertness detection • Wearable and lightweight • Scalable
Development-To-Date: Initial conception (10/01/2024)
Reference: UCLA Case No. 2025-245
Lead Inventor: Ravi S. Aysola, MD, Clinical Professor of Medicine Chief, Sleep Medicine Section Division of Pulmonary, Critical Care and Sleep Medicine Director, UCLA Sleep Disorder Center