With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driv...With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.展开更多
COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath.Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring...COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath.Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers.Mass spectrometry(MS)-based approaches off er a promising analytical platform for human breath analysis due to their high speed,specificity,sensitivity,reproducibility,and broad coverage,as well as its versatile coupling methods with different chromatographic separation,and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19.Herein,we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples,including metabolites,proteins,microorganisms,and elements.New features of breath sampling and analysis are highlighted.Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.展开更多
文摘With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.
基金supported by the National Natural Science Foundation of China(21804053)。
文摘COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath.Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers.Mass spectrometry(MS)-based approaches off er a promising analytical platform for human breath analysis due to their high speed,specificity,sensitivity,reproducibility,and broad coverage,as well as its versatile coupling methods with different chromatographic separation,and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19.Herein,we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples,including metabolites,proteins,microorganisms,and elements.New features of breath sampling and analysis are highlighted.Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.
