Fatigue is a major source of stress and accidents in today's world, but there are no objective ways of monitoring and preventing the build-up of fatigue.

Sleep and wake periods are major factors, but not the only ones, that contribute to regulate the onset of fatigue. In this project, we start by developing a non-intrusive, wearable device for monitoring sleep and wake phases.

Since body signals related to sleep and wake are different from person to person, our device incorporates learning technologies adapted from our work on autonomous robotics. This allows the device to self-tune to the user.

The output of the sleep/wake device will then be incorporated into a model of fatigue that takes into account also other body signals and can adapt to the style and physiology of the user.

A version of the sleep/wake device will be tested within the framework of Solar Impulse, where the pilot has to be alert during the entire flight, which can take up to five days and nights. Our device can be used to predict the pilots fatigue and to calculate his optimal break times, always taking into account the mission status.