Taking a short nap after lunch can help refresh the mind. A Tainan-based research team led by National Cheng Kung University (NCKU) professor Sheng-Fu Liang has developed a smart eye mask to help with taking an ideal snooze.

The smart eye mask can accurately monitor sleep stages and wake the user up at the proper time, according to Liang, professor of Computer Science and Information Engineering.

Liang’s team has developed a portable wireless electrooculography (EOG) recording device integrated into an eye mask.

An EOG-based automatic sleep scoring application (APP) that can correctly recognize all five of the sleep stages online was also developed accompanied with the eye mask.

This system is approaching the stage of commercial production, according to Liang.

He noted, polysomnography (PSG) commonly used to diagnose sleep disorders is a recording made during sleep that uses electroencephalogram (EEG), electromyogram (EMG), electro-oculography (EOG), and other physiology-related measures to evaluate sleep disorders.

However, the excessive number of wired connections for conventional PSG is often a problem that leads to sleep disturbance and cannot be used in daily life.

In comparison with the PSG systems, the design of this eye mask and soft fabric electrodes for EOG recording protects users from interference by the electrode wire and allows them to wear the eye mask conveniently.

This system wirelessly sends the EOG data to the mobile platform and the developed APP can score sleep stages online and wake the user up with an auditory alarm in the proper sleep stage before the preset wake-up time.

For example, the smart eye mask was designed to wake the user in sleep stage 2 (light sleep) and prevent the user from falling to deep sleep during a short-term nap to avoid sleep inertia.

Liang explained, sleep inertia is a physiological state caused by being awakened during a deep sleep stage and this phenomenon can decrease performance and alertness.

This system will not only benefit sleep quality monitoring but also inspire future sleep stage-sensitive (SSS) applications such as to automatically adjust aspects of the sleep environment on the basis of a user’s sleep stages, according to Liang.