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首页> 外文会议>International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation >Large-Scale Sleep Condition Analysis Using Selfies from Social Media
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Large-Scale Sleep Condition Analysis Using Selfies from Social Media

机译：使用社交媒体自拍进行大规模睡眠条件分析

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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.
机译：睡眠状况与个人健康密切相关。诸如睡眠障碍和睡眠不足之类的不良睡眠条件会影响一个人的日常工作，还可能导致许多慢性疾病。已经进行了许多努力来监测人们的睡眠状况。但是，传统方法需要复杂的设备，并且要花费大量时间。在本文中，我们尝试开发一种新颖的方法来通过像医生一样仔细检查面部线索来预测个人的睡眠状况。我们不是直接测量睡眠状况，而是测量间接反映睡眠状况的睡眠不足疲劳。我们的方法可以根据受试者提供的自拍来预测睡眠不足的疲劳率。该速率用于指示睡眠状况。为了更深入地了解人类的睡眠状况，我们从Twitter和Instagram上发布的自拍照中收集了大约100,000张面孔，并使用自动算法识别了他们的年龄，性别和种族。接下来，我们研究了关于年龄，性别和种族的睡眠状况分布。我们的研究表明，在各个年龄段中，0-20岁青年和青少年组的疲劳百分比最高，这表明该年龄段的睡眠状况较差。对于性别而言，女性的疲劳百分比高于男性，这意味着遭受睡眠问题困扰的女性人数要多于男性。在各族裔中，高加索人的疲劳百分比最高，其次是亚裔和非裔美国人。

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