4,879 research outputs found
Multimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201
A systematic review of physiological signals based driver drowsiness detection systems.
Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals. [Abstract copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study
The advent of industrial robotics and autonomous systems endow human-robot
collaboration in a massive scale. However, current industrial robots are
restrained in co-working with human in close proximity due to inability of
interpreting human agents' attention. Human attention study is non-trivial
since it involves multiple aspects of the mind: perception, memory, problem
solving, and consciousness. Human attention lapses are particularly problematic
and potentially catastrophic in industrial workplace, from assembling
electronics to operating machines. Attention is indeed complex and cannot be
easily measured with single-modality sensors. Eye state, head pose, posture,
and manifold environment stimulus could all play a part in attention lapses. To
this end, we propose a pipeline to annotate multimodal dataset of human
attention tracking, including eye tracking, fixation detection, third-person
surveillance camera, and sound. We produce a pilot dataset containing two fully
annotated phone assembly sequences in a realistic manufacturing environment. We
evaluate existing fatigue and drowsiness prediction methods for attention lapse
detection. Experimental results show that human attention lapses in production
scenarios are more subtle and imperceptible than well-studied fatigue and
drowsiness.Comment: 6 page
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