Automatic driver stress level classification using multimodal deep learning

Rastgoo, Mohammad Naim; Nakisa, Bahareh; Maire, Frédéric; Rakotonirainy, Andry; Chandran, Vinod

doi:10.1016/j.eswa.2019.07.010

Cited by 134 publications

(77 citation statements)

References 22 publications

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“…Due to the properties of this type of network for predicting signals over time, many authors use them to work with physiological signals. In this scoping review they have been used as LSTM [62] and ensemble-based methods like CNN+LSTM [62] and adaptive neuro-fuzzy inference system (ANFIS-based short-term) [53].…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Sánchez-Reolid¹,

Mt²,

Fernández-Caballero³

2020

Preprint

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Early detection of stress can prevent us from suffering from a long-term illness such as depression and anxiety. This article presents a scoping review of stress detection based on electrodermal activity (EDA) and machine learning (ML). From an initial set of 395 articles searched in six scientific databases, 58 were finally selected according to various criteria established. The scoping review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, preprocessing, processing and feature extraction. Finally, all the ML techniques applied to the features of this signal have been studied for stress detection. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high performance values. On the contrary, it has been evidenced that unsupervised learning is not very common in the detection of stress through EDA. This scoping review concludes that the use of EDA for the detection of arousal variation (and stress detection) is widely spread, with very good results in its prediction with the ML methods found during this review.

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Section: Supervised Learning Methodsmentioning

confidence: 99%

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Sánchez-Reolid¹,

Mt²,

Fernández-Caballero³

2020

Preprint

View full text Add to dashboard Cite

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“…The DL algorithms are also set up using Python. In detail, the architecture of the CNN used for classification has been inspired by the one proposed in [28], and its parameters are briefly reported in Table 1. The CNN contains two identical sequences of four operations: a convolutional layer, an Exponential Linear Unit (ELU), a batch normalisation layer and a max pooling layer.…”

Section: Classification Algorithmsmentioning

confidence: 99%

“…Their experiments are designed for identification of mental fatigue and for discrimina-tion between driving and resting conditions. A multimodal system based on CNNs and Long Short-Term Memory (LSTM) networks, using ECG, vehicle data, and contextual data has been proposed in [28]. In [29], CNNs are considered to detect a driver's braking intention, in different driving conditions, using EEG data.…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning Techniques for Stress Detection in Car Drivers

Zontone¹,

Affanni²,

Bernardini³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…In the wake of the advances in deep learning techniques, multimodal deep learning approach demonstrated better performance on representation learning and classification 34 . Rastgoo et al 35 proposed a multimodal fusion model for driver stress classification that showed better performance than traditional machine learning methods based on feature engineering. Sentimental analysis is also a successful application domain.…”

Section: Related Workmentioning

confidence: 99%

A multimodal deep architecture for traditional Chinese medicine diagnosis

Dai

Wang

Dai

et al. 2020

Concurrency and Computation

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Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

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Automatic driver stress level classification using multimodal deep learning

Cited by 134 publications

References 22 publications

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Supervised Learning Techniques for Stress Detection in Car Drivers

A multimodal deep architecture for traditional Chinese medicine diagnosis

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