The purpose of this study was to assess the effects of reducing driving fatigue with magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points using heart rate (HR), reaction time (RT) testing, critical ...The purpose of this study was to assess the effects of reducing driving fatigue with magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points using heart rate (HR), reaction time (RT) testing, critical flicker fusion frequency (CFF) and subjective evaluation. Twenty healthy subjects were randomly divided into two groups: A-group (study group) and B-group (control group). All subjects were required to be well rested before the experiment. The subjects were engaged in high speed driving at a constant vehicle velocity of 80 km/h continuously for three hours on a test course simulating an expressway. During the driving magnitopunctures were applied to the Dazhui (DU14) point and Neiguan (PC6) points for the A-group when the subject performed the task for two and half hours, and for the B-group magnitopunctures were applied to non-acupuncture points at the same time session. In this study RT exbited a significant delay in B-group (P<0.01) but no found in A-group after the driving task. CFF and subjective evaluation also exhibited significant differences between the two groups after the driving task (P<0.05). The findings showed that magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points could reduce the effects of driving fatigue.展开更多
为了提高呼吸信号判别驾驶疲劳的准确率,通过模拟驾驶试验探究呼吸信号与驾驶员疲劳状态的关系,提出呼吸疲劳节点的概念,并基于呼吸疲劳节点判别驾驶员的疲劳状态。首先,通过模拟驾驶试验采集驾驶员的呼吸信号,采用Karolinska嗜睡量表(K...为了提高呼吸信号判别驾驶疲劳的准确率,通过模拟驾驶试验探究呼吸信号与驾驶员疲劳状态的关系,提出呼吸疲劳节点的概念,并基于呼吸疲劳节点判别驾驶员的疲劳状态。首先,通过模拟驾驶试验采集驾驶员的呼吸信号,采用Karolinska嗜睡量表(Karolinska sleepiness scale, KSS)对疲劳程度进行主观自评量化。其次,把单位时间内眼睛闭合百分比(percentage of eyelid closure over the pupil over time, PERCLOS)作为参考,与主观自评反馈结合,对驾驶员呼吸疲劳节点进行标定。最后,基于呼吸疲劳节点利用随机树算法(random tree, RT)获得轻/重度呼吸疲劳变化节点的判别模型。结果表明:该模型能更加及时、准确地判别出驾驶员的疲劳状态;基于随机树算法获得的筛选条件对轻度呼吸疲劳变化节点识别的准确性要高于重度呼吸疲劳变化节点;轻/重度呼吸疲劳变化节点的平均识别误差分别为3.50 min和3.66 min,预测准确率分别为92.09%和92.03%。展开更多
文摘The purpose of this study was to assess the effects of reducing driving fatigue with magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points using heart rate (HR), reaction time (RT) testing, critical flicker fusion frequency (CFF) and subjective evaluation. Twenty healthy subjects were randomly divided into two groups: A-group (study group) and B-group (control group). All subjects were required to be well rested before the experiment. The subjects were engaged in high speed driving at a constant vehicle velocity of 80 km/h continuously for three hours on a test course simulating an expressway. During the driving magnitopunctures were applied to the Dazhui (DU14) point and Neiguan (PC6) points for the A-group when the subject performed the task for two and half hours, and for the B-group magnitopunctures were applied to non-acupuncture points at the same time session. In this study RT exbited a significant delay in B-group (P<0.01) but no found in A-group after the driving task. CFF and subjective evaluation also exhibited significant differences between the two groups after the driving task (P<0.05). The findings showed that magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points could reduce the effects of driving fatigue.
文摘为了提高呼吸信号判别驾驶疲劳的准确率,通过模拟驾驶试验探究呼吸信号与驾驶员疲劳状态的关系,提出呼吸疲劳节点的概念,并基于呼吸疲劳节点判别驾驶员的疲劳状态。首先,通过模拟驾驶试验采集驾驶员的呼吸信号,采用Karolinska嗜睡量表(Karolinska sleepiness scale, KSS)对疲劳程度进行主观自评量化。其次,把单位时间内眼睛闭合百分比(percentage of eyelid closure over the pupil over time, PERCLOS)作为参考,与主观自评反馈结合,对驾驶员呼吸疲劳节点进行标定。最后,基于呼吸疲劳节点利用随机树算法(random tree, RT)获得轻/重度呼吸疲劳变化节点的判别模型。结果表明:该模型能更加及时、准确地判别出驾驶员的疲劳状态;基于随机树算法获得的筛选条件对轻度呼吸疲劳变化节点识别的准确性要高于重度呼吸疲劳变化节点;轻/重度呼吸疲劳变化节点的平均识别误差分别为3.50 min和3.66 min,预测准确率分别为92.09%和92.03%。
