摘要
航天员执行操纵和维护设备等任务时需保持高警觉状态,以便应对突发情况,保护自身安全。针对目前警觉度检测尚无统一标准,单生理参数检测法可靠性差的问题,利用多模态参数检测法,采用PVT任务与2-back任务组合诱导警觉度降低,通过功能性近红外光谱技术(fNIRS)和心电技术(ECG)采集14名被试前额部分的氧合血红蛋白(HbO)信号、脱氧血红蛋白(Hb)信号以及ECG信号,并记录被试的行为学数据。结果表明:此实验范式成功诱导警觉度下降,在低警觉度状态下大脑代谢水平增加,复杂度增加,大脑前额区活跃度增加;心率降低,副交感神经活性增强。二者特征相结合增大了警觉度识别三分类准确度。在支持向量机模型下,14名被试的平均三分类正确率达到(80.37±5.76)%,较之前文献报道的正确率有所提升。验证了此特征模型检测警觉度水平的有效性及使用混合特征矩阵提高警觉度模型的鲁棒性。
To deal with emergencies and ensure safety,astronauts need to maintain a high state of vigilance when executing tasks such as operating and maintaining equipment.At present,there is no unified physiological standard for alertness detection,while single-physiological parameter detection method has many interference factors and its reliability is difficult to be guaranteed,so multimodal parameter detection method has become a trend.In his paper,the combination of PVT task and 2-back task was used to reduce alertness.Functional near-infrared spectroscopy(fNIRS)was used to collect oxygenated hemoglobin(HbO)signals and deoxygenated hemoglobin(Hb)signals in the forehead of 14 subjects.The participant’s ECG signals were collected through electrocardiography.In addition,the participants’behavioral data were also recorded.The results showed that this experimental paradigm successfully induced the decrease of alertness.In the state of low alertness,the brain metabolic level and the brain complexity increased,while the activity of the prefrontal area in-creased.The heart rate decreased and the activity of the parasympathetic nerve was enhanced.The combination of ECG and fNIRS features increased the three-classification accuracy of alertness recognition.Under the support vector machine model,the average three-classification accuracy of 14 subjects reached(80.37±5.76)%,which was improved compared with the previous literature report.It was verified that this feature model could effectively detect the alertness level,and the use of a hybrid feature matrix also improved the robustness of the alertness model.
作者
王璐琪
姜劲
孙子恒
代艳莹
曹勇
焦学军
周鹏
WANG Luqi;JIANG Jin;SUN Ziheng;DAI Yanying;CAO Yong;JIAO Xuejun;ZHOU Peng(School of Precision Instrument and Opto-Electronics Engineering,Tianjin University,Tianjin 300072,China;Academy of Medical Engineering and Translational Medicine,Tianjin University,Tianjin 300072,China;National Key Laboratory of Human Factors Engineering,China Astronaut Research and Training Center,Beijing 100094,China)
出处
《载人航天》
CSCD
北大核心
2023年第2期177-185,共9页
Manned Spaceflight
基金
航天医学实验项目(HYZHXM03007)
人因工程重点实验室自主研究基金(SYFD061903)。
