A multimodal dataset for various forms of distracted driving

Taamneh, Salah; Tsiamyrtzis, Panagiotis; Dcosta, Malcolm; Buddharaju, Pradeep; Khatri, Ashik; Manser, Michael; Ferris, Thomas K.; Wunderlich, Robert; Pavlidis, Ioannis

doi:10.1038/sdata.2017.110

Cited by 77 publications

(57 citation statements)

References 18 publications

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“…The original OPPORTUNITY dataset itself has some parts of missing data due to the disconnection of wireless sensor devices [15]. The missing rates are 8.0%, 4.1%, and 4.1% for the [12], mice protein expression dataset [22], and multimodal dataset for distracted driving [44]. These strongly support our research problem that handling missing data is crucial in the real-world applications for multimodal data integration.…”

Section: Missing Block-wise Datasupporting

confidence: 62%

EmbraceNet: A robust deep learning architecture for multimodal classification

2019

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Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper, we propose a novel deep learning-based multimodal fusion architecture for classification tasks, which guarantees compatibility with any kind of learning models, deals with cross-modal information carefully, and prevents performance degradation due to partial absence of data. We employ two datasets for multimodal classification tasks, build models based on our architecture and other state-of-the-art models, and analyze their performance on various situations. The results show that our architecture outperforms the other multimodal fusion architectures when some parts of data are not available.

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Section: Missing Block-wise Datasupporting

confidence: 62%

EmbraceNet: A robust deep learning architecture for multimodal classification

2019

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“…For undertaking steps (i) and (ii) several different strategies are used. For example, during step (i) different modalities like, physiological signals [5,7,8], speech [9] and facial-expressions [10,11] can be acquired. Similarly, the approaches to step (ii) usually vary along the following two main aspects.…”

mentioning

confidence: 99%

A dataset of continuous affect annotations and physiological signals for emotion analysis

et al. 2019

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From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, a direct and real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were obtained from ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature sensors. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.

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“…In this paper, we used a publicly available dataset which was obtained as a result of controlled experiments on distracted driving using the driving simulator shown in Figure 2 [26,33,34]. For these controlled experiments, the subjects were recruited from a local community (population about 250,000) through email solicitations and flier postings.…”

Section: The Datasetmentioning

confidence: 99%

“…Two different controlled experiments were run using the driving simulator to obtain this multimodal dataset, as explained in references [26,33,34]. Experiment 1 was based on a crossover design repeated measures design, such that each experimental unit (subject) received different treatments (stressor) during the different time periods.…”

Section: Two Controlled Experiments In Failure Drivementioning

confidence: 99%

Causality Effects of Interventions and Stressors on Driving Behaviors under Typical Conditions

Gomez

Akleman

et al. 2018

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In this paper, we demonstrate that interventions and stressors do not necessarily cause the same distractions in all people; therefore, it is impossible to evaluate the impacts of interventions and stressors on traffic accidents. We analyzed publicly available multimodal data that was collected through one of the largest controlled experiments on distracted driving. A crossover design was used to examine the effects of actual and perceived interventions and stressors in driving behaviors and parallel designs on reactivity to a startling event. To analyze this data and make recommendations, we developed and compared a wide variety of mixed effects statistical models and machine learning methods to evaluate the effects of interventions and stressors on driving behaviors.

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A multimodal dataset for various forms of distracted driving

Cited by 77 publications

References 18 publications

EmbraceNet: A robust deep learning architecture for multimodal classification

EmbraceNet: A robust deep learning architecture for multimodal classification

A dataset of continuous affect annotations and physiological signals for emotion analysis

Causality Effects of Interventions and Stressors on Driving Behaviors under Typical Conditions

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