基于面压力传感器的驾驶员疲劳监测方法

为预防交通事故的发生,研究一种基于面压力传感器的无仪器佩戴式驾驶员疲劳识别方法.设计实施一种通过安装在驾驶员靠背上的面压力传感器获取压力信号的试验,将面压传感器时域数据进行频域信号处理,通过心电信号与面压信号的相干函数计算,验证面压力传感器信号中蕴藏心电信号的结论.通过对与心电信号相干系数进行降噪、心率变异性(heart rate variability, HRV)数据处理及数据修正,提取出和心率变异性信号LF值一致的结论.试验结果表明,利用面压传感器进行驾驶疲劳报警是准确的.     

Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator

Mental fatigue is a contributing factor to some serious transportation crashes. In this study, we measured mental fatigue in drivers using electroencephalogram (EEG) and electrocardiograph (ECG). Together, thirteen healthy subjects performed a continuous simulated driving task for 90 min with simultaneous ECG and multi-channel EEG recording of each subject. Several important physiological parameters were investigated using preprocessed ECG and EEG signals. The results show that the EEG alpha and beta, the relative power, the amplitude of P300 wave of event-related potential (ERP), the approximated entropy of the ECG, and the lower and upper bands of power of heart rate variability (HRV) are significantly different before and after finishing the driving task (p < 0.05). These metrics are possible indices for measuring simulated driving mental fatigue.     

两类情感状态下心电与心率变异性信号的非线性分析

针对平静（无情感激发状态）和恐惧（有情感激发状态）这两类情感状态,采集了其心电信号数据样本,研究了有情感和没情感状态下心电信号中的情感信息。通过分析这两类情感状态下的心电信号的QT间期对RR间期的散点图、功率谱图的1/f分布以及心率变异性（HRV）信号的Poincare截面图,计算得到了这些非线性特征的关于两类情感状态的分类识别率,同时与心电信号统计特征的识别率做了对比。发现此种提取心电信号非线性特征的方法较于之前的统计特征方法,在识别有情感和没情感这两类情感状态时具有较好的识别效果。     

HRV3控制对于提高加工精度的研究

阐述了伺服系统HRV控制方式的原理及其对改善加工性能方面的特性,并介绍HRV3控制方式的设定方法,利用实验的方式验证了该控制方式对试件加工精度的提高及加工速度的改善。     

Gender differences in acute alcohol effects on self-regulation of arousal in response to emotional and alcohol-related picture cues.

Basic mechanisms through which men and women self-regulate arousal have received little attention in human experimental addiction research, although stress-response-dampening and craving theories suggest an important role of emotional arousal in motivating alcohol use. This study examined gender differences in the effects of acute alcohol intoxication on psychophysiological and self-reported arousal in response to emotionally negative, positive, and neutral, and alcohol-related, picture cues. Thirty-six social drinkers (16 women) were randomly assigned to an alcohol, placebo, or control beverage group and exposed to picture cues every 10 s (0.1 Hz presentation frequency). Psychophysiological arousal was assessed via a 0.1-Hz heart rate variability (HRV) index. A statistically significant beverage group-by-gender interaction effect on psychophysiological, but not self-reported, arousal was found. The 0.1-Hz HRV responses to picture cues were suppressed by alcohol only in men. This gender-specific suppression pattern did not differ significantly across picture cue types. There were no significant gender differences in the placebo or control group. Greater dampening of arousal by alcohol intoxication in men, compared with women, may contribute to men’s greater tendency to use alcohol to cope with stress. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于心冲击图和BP神经网络的心率异常分类研究

心率变异性(Heart rate variability,HRV)被广泛用于临床自主神经系统评估和心率异常分类,传统的HRV分析基于心电图(Electrocardiogram,ECG)、光容积图(Photoplethysmography,PPG)和远程光容积图(Remote PPG,RPPG),这些方法存在诸多不足:(...     

驾驶疲劳状态波动性特征的识别方法

针对疲劳状态变化的波动性特征,基于心率变异性指标构建了一种驾驶疲劳状态识别方法.以驾驶行为绩效为疲劳客观测评指标,给出了适应疲劳波动性特征的驾驶疲劳分级方法.以心率变异性的3项时域指标、5项频域指标为特征因子构建驾驶疲劳识别特征向量,结合支持向量机提出了一种适应小样本的驾驶疲劳状态识别模型.采用10名驾驶员连续4 h的驾驶行为绩效与心电数据,对模型方法予以了测试.测试结果表明:10名驾驶员1级、2级疲劳状态的正确识别率介于70%~82%,平均正确识别率为75%.     

基于乘客感受的沥青路面平整度评价方法

从道路使用者的角度对乘车舒适性与路面平整度之间的关系进行研究.以乘客的心电指标RMSSD(rootmean square of the squared differences of successive n-n intervals)为主要评价依据,选取平整度范围为1.06~5.53 m/km的12条沥青路面进行室外乘车试验.实验数据表明:不同速度下RMSSD随平整度的变化有明显的变化规律,同时结合主观打分法和振动加速度评价法,提出基于乘客舒适性的平整度临界值.     

A physiological data-driven model for learners' cognitive load detection using HRV-PRV feature fusion and optimized XGBoost classification

Due to the increasing attention to online learning, cognitive load has been recently considered as a crucial indicator for judging teenagers' learning state so as to improve both learning and teaching effects. However, some traditional cognitive load measurement methods such as subjective measurement are easily influenced by subjective sensation deviation of subjects. None of them can reflect the cognitive load of learners more precisely. Recently, machine learning–based data modeling has gained more importance in the scenarios of various smart wearables and Internet of things applications. Meanwhile, physiological signals have proven to contribute much to human health assessment. On the basis of the above considerations, this paper proposes a physiological data-driven model for learners' cognitive load detection under the application of smart wearables. The model consists of four modules: physiological signal acquisition, signal preprocessing, heart rate variability and pulse rate variability feature fusion, and cognitive load classification through an optimized extreme gradient boosting classifier in which hyperparameters are adaptively tuned with sequential model–based optimization. Furthermore, we design an experimental paradigm for signal acquisition in a learning environment, and the experimental results demonstrate that the proposed model for cognitive load detection outperforms conventional approaches that only employ either heart rate variability or pulse rate variability for modeling. We also compare the effects of different feature fusion algorithms combined with different classification algorithms, which demonstrates that the proposed model achieves the highest accuracy of cognitive load detection due to its optimal combination of feature fusion and classification.     

穿戴式生理信号检测与分析系统的实现

系统实现了一种穿戴式生理信号检测与分析装置,可实现人体的基本生理参数实时检测。以STM32L152单片机为主控制器,AM2520与APDS-9008组成的光电传感器和MLX90615红外传感器作为核心采集模块,分别采集人体的PPG信号和皮温信号,并通过蓝牙模块HJ-580将数据传至安卓智能手机,用手机作为客户端实时显示心率、呼吸、皮温和心率变异性的结果。目前各模块已经PCB制板并调试完成,结果表明,该系统具有功能多样性、易穿戴等特点。