iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass

Citation:
Soha, Rostaminia, Mayberry Addison, Ganesan Deepak, Marlin Benjamin, and Gummeson Jeremy. "iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 1 (2017): 23.

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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