ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2017
iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Electronics-Free Visual Monitoring of Dry Eye Disease-Related Tear Biomarkers Using Smart Scleral Lenses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Coarse Eyeball Direction Recognition from Eyelid Skin Deformation Using Infrared Distance Sensors on Eyewear.Sensors (Basel, Switzerland) · 2026Article
- A Framework for Selecting and Assessing Wearable Sensors Deployed in Safety Critical Scenarios.Sensors (Basel, Switzerland) · 2024Review
- An Electro-Oculogram (EOG) Sensor's Ability to Detect Driver Hypovigilance Using Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring.Molecules (Basel, Switzerland) · 2021Review
- Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning.Frontiers in artificial intelligence · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
The ability to monitor eye closures and blink patterns has long been known to enable accurate assessment of fatigue and drowsiness in individuals. Many measures of the eye are known to be correlated with fatigue including coarse-grained measures like the rate of blinks as well as fine-grained measures like the duration of blinks and the extent of eye closures. Despite a plethora of research validating these measures, we lack wearable devices that can continually and reliably monitor them in the natural environment. In this work, we present a low-power system, iLid, that can continually sense fine-grained measures such as blink duration and Percentage of Eye Closures (PERCLOS) at high frame rates of 100fps. We present a complete solution including design of the sensing, signal processing, and machine learning pipeline; implementation on a prototype computational eyeglass platform; and extensive evaluation under many conditions including illumination changes, eyeglass shifts, and mobility. Our results are very encouraging, showing that we can detect blinks, blink duration, eyelid location, and fatigue-related metrics such as PERCLOS with less than a few percent error.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.