Title: Large-Scale Sleep Condition Analysis Using Selfies from Social Media,
Abstract: Sleep condition is closely related to an individual's health. Poor sleep
conditions such as sleep disorder and sleep deprivation affect one's daily
performance, and may also cause many chronic diseases. Many efforts have been
devoted to monitoring people's sleep conditions. However, traditional
methodologies require sophisticated equipment and consume a significant amount
of time. In this paper, we attempt to develop a novel way to predict
individual's sleep condition via scrutinizing facial cues as doctors would.
Rather than measuring the sleep condition directly, we measure the
sleep-deprived fatigue which indirectly reflects the sleep condition. Our
method can predict a sleep-deprived fatigue rate based on a selfie provided by
a subject. This rate is used to indicate the sleep condition. To gain deeper
insights of human sleep conditions, we collected around 100,000 faces from
selfies posted on Twitter and Instagram, and identified their age, gender, and
race using automatic algorithms. Next, we investigated the sleep condition
distributions with respect to age, gender, and race. Our study suggests among
the age groups, fatigue percentage of the 0-20 youth and adolescent group is
the highest, implying that poor sleep condition is more prevalent in this age
group. For gender, the fatigue percentage of females is higher than that of
males, implying that more females are suffering from sleep issues than males.
Among ethnic groups, the fatigue percentage in Caucasian is the highest
followed by Asian and African American.