The biomedical hypothesis proposed here is that the immediate trigger for a yawn is a restricted collapse of a few alveoli in the lungs. The extent of this alveolar collapse may be too small for it to be detected by c...The biomedical hypothesis proposed here is that the immediate trigger for a yawn is a restricted collapse of a few alveoli in the lungs. The extent of this alveolar collapse may be too small for it to be detected by current X-ray technology, but this technology is continually improving and may soon be good enough to test the hypothesis. In support of the hypothesis, it is shown that yawning can be inhibited by deep breaths of air, nitrogen or carbogen, thus showing that yawning is not triggered by lack of oxygen or by excess carbon dioxide, leaving alveolar collapse as the most likely possibility. A more extensive form of alveolar collapse is termed atelectasis and this involves a serious state of hypoxia which, if deepened or prolonged, can be fatal. Therefore, if the hypothesis is correct, yawning may prevent the development of atelectasis and save lives. This paper is not concerned with other indirect ways in which yawning may be induced, nor with the mechanism and neural circuitry of the yawn, nor with social aspects of yawning, only with the immediate trigger. My aim is to get better evidence for the hypothesis put forward here and also to study the behaviour of the pulmonary alveoli in normal respiration.展开更多
Numerous theories as to why we yawn have been advanced, but yawning appears to be a complex phenomenon that is difficult to study. The physiology of yawning with its sharp chest inhalation, along with its contagious n...Numerous theories as to why we yawn have been advanced, but yawning appears to be a complex phenomenon that is difficult to study. The physiology of yawning with its sharp chest inhalation, along with its contagious nature, can be explained by an arousal theory, that is, yawning promotes vigilance and alertness within a group that overrides a drive to sleep. Other theories of yawning, such as brain thermoregulation, are not necessarily excluded by the arousal theory but may be the results of yawning rather than its cause.展开更多
词数:506阅读难度:建议阅读时间:6分钟You know the feeling. It’s impossible to resist. You just need to yawn. A yawn consists of an extended gaping of the mouth followed by closing rapidly. In mammals and birds, a long ...词数:506阅读难度:建议阅读时间:6分钟You know the feeling. It’s impossible to resist. You just need to yawn. A yawn consists of an extended gaping of the mouth followed by closing rapidly. In mammals and birds, a long intake of breath and shorter outlet follows the gaping of the mouth, but in other species such as fish,amphibians(两栖动物)and snakesthere is no intake of breath.展开更多
Every person yawns.So do many other vertebrate animals.While yawning is contagious,not everyone catches a yawn.Around 60%-70% of people yawn if they see another person yawn in real life or in a photo or even read ahou...Every person yawns.So do many other vertebrate animals.While yawning is contagious,not everyone catches a yawn.Around 60%-70% of people yawn if they see another person yawn in real life or in a photo or even read ahout yawning.展开更多
空中交通管制员在指挥飞机时存在频繁嘴部开合活动。为从管制员的嘴部陆空通话行为中准确区分哈欠行为,降低管制员疲劳工作产生的安全风险,提出了一种基于视频的结合卷积神经网络(Convolutional Neural Network,CNN)与长短期记忆网络(Lo...空中交通管制员在指挥飞机时存在频繁嘴部开合活动。为从管制员的嘴部陆空通话行为中准确区分哈欠行为,降低管制员疲劳工作产生的安全风险,提出了一种基于视频的结合卷积神经网络(Convolutional Neural Network,CNN)与长短期记忆网络(Long and Short Term Memory Networks,LSTM)的管制员嘴部行为识别方法。首先,搭建面部定位模型提取人脸68特征点,建立嘴部几何区域提取模型划分嘴部区域;其次,建立管制员哈欠检测模型分别提取嘴部视频序列帧的空间特征与时间特征;最后,采集数据集管制员嘴部活动数据集(Civil Aviation University of China-Controller,CAUC CON)用于模型训练,通过哈欠分类模型得出序列帧内管制员嘴部哈欠识别结果。结果表明:基于视频的加入时间信息的哈欠检测方法更适合管制员的工作条件,较传统哈欠识别方法的平均识别准确率最高提升了14.4%。展开更多
Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sle...Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing accidents.This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle.Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is achieved.One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement agencies.The suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.展开更多
文摘The biomedical hypothesis proposed here is that the immediate trigger for a yawn is a restricted collapse of a few alveoli in the lungs. The extent of this alveolar collapse may be too small for it to be detected by current X-ray technology, but this technology is continually improving and may soon be good enough to test the hypothesis. In support of the hypothesis, it is shown that yawning can be inhibited by deep breaths of air, nitrogen or carbogen, thus showing that yawning is not triggered by lack of oxygen or by excess carbon dioxide, leaving alveolar collapse as the most likely possibility. A more extensive form of alveolar collapse is termed atelectasis and this involves a serious state of hypoxia which, if deepened or prolonged, can be fatal. Therefore, if the hypothesis is correct, yawning may prevent the development of atelectasis and save lives. This paper is not concerned with other indirect ways in which yawning may be induced, nor with the mechanism and neural circuitry of the yawn, nor with social aspects of yawning, only with the immediate trigger. My aim is to get better evidence for the hypothesis put forward here and also to study the behaviour of the pulmonary alveoli in normal respiration.
文摘Numerous theories as to why we yawn have been advanced, but yawning appears to be a complex phenomenon that is difficult to study. The physiology of yawning with its sharp chest inhalation, along with its contagious nature, can be explained by an arousal theory, that is, yawning promotes vigilance and alertness within a group that overrides a drive to sleep. Other theories of yawning, such as brain thermoregulation, are not necessarily excluded by the arousal theory but may be the results of yawning rather than its cause.
文摘词数:506阅读难度:建议阅读时间:6分钟You know the feeling. It’s impossible to resist. You just need to yawn. A yawn consists of an extended gaping of the mouth followed by closing rapidly. In mammals and birds, a long intake of breath and shorter outlet follows the gaping of the mouth, but in other species such as fish,amphibians(两栖动物)and snakesthere is no intake of breath.
文摘Every person yawns.So do many other vertebrate animals.While yawning is contagious,not everyone catches a yawn.Around 60%-70% of people yawn if they see another person yawn in real life or in a photo or even read ahout yawning.
文摘空中交通管制员在指挥飞机时存在频繁嘴部开合活动。为从管制员的嘴部陆空通话行为中准确区分哈欠行为,降低管制员疲劳工作产生的安全风险,提出了一种基于视频的结合卷积神经网络(Convolutional Neural Network,CNN)与长短期记忆网络(Long and Short Term Memory Networks,LSTM)的管制员嘴部行为识别方法。首先,搭建面部定位模型提取人脸68特征点,建立嘴部几何区域提取模型划分嘴部区域;其次,建立管制员哈欠检测模型分别提取嘴部视频序列帧的空间特征与时间特征;最后,采集数据集管制员嘴部活动数据集(Civil Aviation University of China-Controller,CAUC CON)用于模型训练,通过哈欠分类模型得出序列帧内管制员嘴部哈欠识别结果。结果表明:基于视频的加入时间信息的哈欠检测方法更适合管制员的工作条件,较传统哈欠识别方法的平均识别准确率最高提升了14.4%。
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number MoE-IF-UJ-22-4100409-1.
文摘Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing accidents.This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle.Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is achieved.One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement agencies.The suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.
