Next Article in Journal
Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models
Previous Article in Journal
Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks
Open AccessFeature PaperArticle

Distracted and Drowsy Driving Modeling Using Deep Physiological Representations and Multitask Learning

Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2021, 11(1), 88; https://doi.org/10.3390/app11010088
Received: 18 November 2020 / Revised: 11 December 2020 / Accepted: 19 December 2020 / Published: 24 December 2020
(This article belongs to the Special Issue Pattern Recognition in Multimedia Signal Analysis)
In this paper, we investigated various physiological indicators on their ability to identify distracted and drowsy driving. In particular, four physiological signals are being tested: blood volume pulse (BVP), respiration, skin conductance and skin temperature. Data were collected from 45 participants, under a simulated driving scenario, through different times of the day and during their engagement on a variety of physical and cognitive distractors. We explore several statistical features extracted from those signals and their efficiency to discriminate between the presence or not of each of the two conditions. To that end, we evaluate three traditional classifiers (Random Forests, KNN and SVM), which have been extensively applied by the related literature and we compare their performance against a deep CNN-LSTM network that learns spatio-temporal physiological representations. In addition, we explore the potential of learning multiple conditions in parallel using a single machine learning model, and we discuss how such a problem could be formulated and what are the benefits and disadvantages of the different approaches. Overall, our findings indicate that information related to the BVP data, especially features that describe patterns with respect to the inter-beat-intervals (IBI), are highly associates with both targeted conditions. In addition, features related to the respiratory behavior of the driver can be indicative of drowsiness, while being less associated with distractions. Moreover, spatio-temporal deep methods seem to have a clear advantage against traditional classifiers on detecting both driver conditions. Our experiments show, that even though learning both conditions jointly can not compete directly to individual, task-specific CNN-LSTM models, deep multitask learning approaches have a great potential towards that end as they offer the second best performance on both tasks against all other evaluated alternatives in terms of sensitivity, specificity and the area under the receiver operating characteristic curve (AUC). View Full-Text
Keywords: diver monitoring; multitask learning; machine learning; deep learning diver monitoring; multitask learning; machine learning; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Papakostas, M.; Das, K.; Abouelenien, M.; Mihalcea, R.; Burzo, M. Distracted and Drowsy Driving Modeling Using Deep Physiological Representations and Multitask Learning. Appl. Sci. 2021, 11, 88. https://doi.org/10.3390/app11010088

AMA Style

Papakostas M, Das K, Abouelenien M, Mihalcea R, Burzo M. Distracted and Drowsy Driving Modeling Using Deep Physiological Representations and Multitask Learning. Applied Sciences. 2021; 11(1):88. https://doi.org/10.3390/app11010088

Chicago/Turabian Style

Papakostas, Michalis; Das, Kapotaksha; Abouelenien, Mohamed; Mihalcea, Rada; Burzo, Mihai. 2021. "Distracted and Drowsy Driving Modeling Using Deep Physiological Representations and Multitask Learning" Appl. Sci. 11, no. 1: 88. https://doi.org/10.3390/app11010088

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop