A Driver State Detection System—Combining a Capacitive Hand Detection Sensor With Physiological Sensors

Mühlbacher-Karrer, Stephan; Mosa, Ahmad Haj; Faller, Lisa-Marie; Ali, Mouhannad; Hamid, Raiyan; Zangl, Hubert; Kyamakya, Kyandoghere

doi:10.1109/tim.2016.2640458

Cited by 76 publications

(36 citation statements)

References 52 publications

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“…In this chapter, a Cellular Neural Network (CNN) based classification is introduced. The proposed CNN architecture is an improved version of our previous works [ 2 , 13 ]. CNN was suggested by Chua and Yang (1988) [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

“…It plays an important role in decision-making, behavior and other social communication. The ability to understand and recognize human emotion has been identified as one of the key focus areas listed by research groups in different fields of intelligent systems [ 1 ] such as safe driving [ 2 ], health care [ 3 ], social security [ 4 ], multimedia digital entertainment [ 5 ] and other fields. Moreover, human emotions can be extracted from measured appropriate physiological sensor date.…”

Section: Introductionmentioning

confidence: 99%

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A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

Ali¹,

Machot

Mosa³

et al. 2018

Sensors

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Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which are more universal and where the emotion recognition system is trained using a specific group of subjects and then tested on totally new persons and thereby possibly while using other sensors of same physiological signals in order to recognize their emotions. In this paper, we explore a novel robust subject-independent human emotion recognition system, which consists of two major models. The first one is an automatic feature calibration model and the second one is a classification model based on Cellular Neural Networks (CNN). The proposed system produces state-of-the-art results with an accuracy rate between 80% and 89% when using the same elicitation materials and physiological sensors brands for both training and testing and an accuracy rate of 71.05% when the elicitation materials and physiological sensors brands used in training are different from those used in training. Here, the following physiological signals are involved: ECG (Electrocardiogram), EDA (Electrodermal activity) and ST (Skin-Temperature).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

Ali¹,

Machot

Mosa³

et al. 2018

Sensors

Self Cite

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“…However, the effects of hand, arm, and body on the recognition of driver status were not discussed. Similarly, a stress detection system for drivers was studied in [34]. A specific type of continuous recurrent neural network named cellular neural network was used for the binary classification task.…”

Section: B Related Workmentioning

confidence: 99%

Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition

Xing

Zhang

Wang

et al. 2018

IEEE Trans. Comput. Soc. Syst.

127

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Driver decisions and behaviours regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behaviour and assist in driving tasks according to their status. In this study, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this study. The tasks include normal driving, left, right, and rear mirror-checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks while the other three tasks are regarded as dangerous and distracting tasks. The driver behaviour signals collected from the Kinect consist of a colour and depth image of the driver inside the vehicle cabin. Additionally, three-dimensional head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features to behaviour recognition is evaluated using Random Forests (RF) and Maximal Information Coefficient (MIC) methods. Next, a Feedforward Neural Network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behaviour recognition.

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“…Crema et al [5] present a solution that exploits smartphones to monitor driver vital parameters and measure his stress. In an alternative approach, wearable devices can be equipped with specific sensors [6,7]. For the same purpose, Deng et al [8] propose a vision-based system.…”

Section: Introductionmentioning

confidence: 99%

A Wearable Smart Device to Monitor Multiple Vital Parameters—VITAL ECG

2020

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Smart devices are more and more present in every aspect of everyday life. From smartphones, which are now like mini-computers, through systems for monitoring sleep or fatigue, to specific sensors for the recording of vital parameters. A particular class of the latter regards health monitoring. Indeed, through the use of such devices, several vital parameters can be acquired and automatically monitored, even remotely. This paper presents the second generation of VITAL-ECG, a smart device designed to monitor the most important vital parameters as a "one touch" device, anywhere, at low cost. It is a wearable device that coupled with a mobile app can track bio-parameters such as: electrocardiogram, SpO 2 , skin temperature, and physical activity of the patient. Even if it not yet a medical device, a comprehensive comparison with a golden standard electrocardiograph is presented to demonstrate the quality of the recorded signals and the validity of the proposed approach.

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A Driver State Detection System—Combining a Capacitive Hand Detection Sensor With Physiological Sensors

Cited by 76 publications

References 52 publications

A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition

A Wearable Smart Device to Monitor Multiple Vital Parameters—VITAL ECG

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