Emotion Recognition in Car Industry

Katsis, Christos D.; Rigas, George; Goletsis, Yorgos; Fotiadis, Dimitrios I.

doi:10.1002/9781118910566.ch20

Cited by 26 publications

(18 citation statements)

References 84 publications

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“…Just like stress, confusion or irritation leads to impairment of driving capabilities including driver’s perception, attention, decision making, and strategic planning. Nervousness corresponds to a level of arousal above the “normal” one, which best suits to the driving task [211]. It is an affective state with negative impact both on decision-making process and strategic planning.…”

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

“…It is an affective state with negative impact both on decision-making process and strategic planning. Nervousness can be induced by a variety of reasons either directly related to the driving task like novice drivers or by other factors like personal/physical conditions [211]. …”

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

“…In other words, happy drivers are better drivers [208,210]. Facial expression and emotion recognition can be used in advanced car safety systems, which, on one hand, can identify hazardous emotional drivers’ states that can lead to distraction and, on the other, can provide tailored (according to each state and associated hazards) suggestions and warnings to the driver [211]. …”

Section: The Relationship Between Facial Expressions and Distractionmentioning

confidence: 99%

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Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

105

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

Section: The Relationship Between Facial Expressions and Distractionmentioning

confidence: 99%

See 1 more Smart Citation

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

105

View full text Add to dashboard Cite

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“…The frequency sampling 240 Hz was chosen as it more than twice as fast as the highest frequency component that we want to maintain after sampling. Notably, the frequency range of the SCR signal is 0.5 to 2 Hz [1], [11].…”

Section: System Design and Stimulus Protocolmentioning

confidence: 99%

“…Advance driver assistance systems [1], intelligent transport system [2], and in-vehicle driver behaviors warning systems [3] have slowly penetrated in the real-world implementation because of the high cost of installing such systems in public cars.…”

Section: Introductionmentioning

confidence: 99%

Low Cost Wearable Sensor for Human Emotion Recognition Using Skin Conductance Response

Minhad¹,

Ooi

Ali³

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYMalaysia is one of the countries with the highest car crash fatality rates in Asia. The high implementation cost of in-vehicle driver behavior warning system and autonomous driving remains a significant challenge. Motivated by the large number of simple yet effective inventions that benefitted many developing countries, this study presents the findings of emotion recognition based on skin conductance response using a lowcost wearable sensor. Emotions were evoked by presenting the proposed display stimulus and driving stimulator. Meaningful power spectral density was extracted from the filtered signal. Experimental protocols and frameworks were established to reduce the complexity of the emotion elicitation process. The proof of concept in this work demonstrated the high accuracy of two-class and multiclass emotion classification results. Significant differences of features were identified using statistical analysis. This work is one of the most easy-to-use protocols and frameworks, but has high potential to be used as biomarker in intelligent automobile, which helps prevent accidents and saves lives through its simplicity. key words: electrodermal activity, power spectral density, process stimuli, skin conductance response

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